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ERAD 2022 Open Radar Science Shortcourse

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This tutorial covers how to get started with the Open Radar Science stack!

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Motivation

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The course will take place on 28 August 2022, the day before before the 2022 ERAD Radar Conference. We will introduce the participants to community software packages designed for radar data processing, including (but not limited to) BALTRAD, LROSE, Py-ART, and wradlib. Following a welcome, there will be an introduction to Open Science concepts with the Open Radar context.

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The common ground for most of those tools is Python, so we’ll feature a quick intro to the Python programming language, and endow participants with the basics of how to contribute to community software.

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List of Instructors

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  • Scott Collis (Argonne National Laboratory, USA)

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  • Bobby Jackson (Argonne National Laboratory, USA)

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  • Maxwell Grover (Argonne National Laboratory, USA)

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  • Daniel Michelson (Environment and Climate Change Canada)

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  • Jordi Figueras i Ventura (Météo-France, France)

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  • Daniel Wolfensberger (MeteoSwiss, Switzerland)

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  • Mike Dixon (National Center for Atmospheric Research, USA)

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  • Kai Mühlbauer (University of Bonn, Germany)

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  • Velibor Pejčić (University of Bonn, Germany)

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Course program

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  • 09:00 - 09:15 Welcome and getting started

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  • 09:15 - 09:45 Community weather radar software and Open Science

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  • 09:45 - 10:30 Overview of the open source radar processing packages

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  • 10:30 - 11:00 Coffee break

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  • 11:00 - 11:45 Hands on Py-ART

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  • 11:45 - 12:30 Hands on wradlib

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  • 12:30 - 13:30 Lunch break

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  • 13:30 - 14:15 Hands on BALTRAD BALTRAD

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  • 14:15 - 15:00 Hands on LROSE

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  • 15:00 - 15:30 Coffee break

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  • 15:30 - 16:00 Combining multiple packages

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  • 16:00 - 16:30 Becoming a developer in an open source project, best practices

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  • 16:30 - 17:00 Open slot, discussion, evaluation

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Structure

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Tool Foundations

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Content relevant to each of the Open Radar packages (ex. Py-ART, wradlib, LROSE, BALTRAD).

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Example Workflows

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Workflows utilizing the various packages and open radar data.

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Things You Need to Prepare

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Participants need to bring their own 64-bit notebook (Linux, Windows, Mac). The exercices will take place on a cloud server. On Windows, the use of a ssh-client such as Putty or MobaXterm will be necessary.

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Getting Started

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b/_preview/104/_sources/README.md @@ -0,0 +1,57 @@ +thumbnail + +# ERAD 2022 Open Radar Science Shortcourse + +[![nightly-build](https://github.com/openradar/erad2022/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/openradar/erad2022/actions/workflows/nightly-build.yaml) +[![Binder](http://binder.projectpythia.org/badge_logo.svg)](http://binder.projectpythia.org/v2/gh/openradar/erad2022/main?labpath=notebooks) + +This tutorial covers how to get started with the Open Radar Science stack! + +## Motivation + +The course will take place on 28 August 2022, the day before before the [2022 ERAD Radar Conference](https://www.erad2022.ch/). We will introduce the participants to [community software packages](http://openradarscience.org) designed for radar data processing, including (but not limited to) [BALTRAD](https://github.com/baltrad), [LROSE](http://lrose.net/), [Py-ART](http://arm-doe.github.io/pyart/), and [wradlib](https://wradlib.org). Following a welcome, there will be an introduction to Open Science concepts with the Open Radar context. + +The common ground for most of those tools is Python, so we'll feature a quick intro to the Python programming language, and endow participants with the basics of how to contribute to community software. + +## List of Instructors +* Scott Collis (Argonne National Laboratory, USA) +* Bobby Jackson (Argonne National Laboratory, USA) +* Maxwell Grover (Argonne National Laboratory, USA) +* Daniel Michelson (Environment and Climate Change Canada) +* Jordi Figueras i Ventura (Météo-France, France) +* Daniel Wolfensberger (MeteoSwiss, Switzerland) +* Mike Dixon (National Center for Atmospheric Research, USA) +* Kai Mühlbauer (University of Bonn, Germany) +* Velibor Pejčić (University of Bonn, Germany) + +### Contributors + + + + + +## Course program +* 09:00 - 09:15 Welcome and getting started +* 09:15 - 09:45 Community weather radar software and Open Science +* 09:45 - 10:30 [Overview of the open source radar processing packages](package-overview/README.md) +* 10:30 - 11:00 Coffee break +* 11:00 - 11:45 Hands on Py-ART +* 11:45 - 12:30 Hands on [wradlib](wradlib/README.md) +* 12:30 - 13:30 Lunch break +* 13:30 - 14:15 Hands on BALTRAD [BALTRAD](baltrad/README.md) +* 14:15 - 15:00 Hands on LROSE +* 15:00 - 15:30 Coffee break +* 15:30 - 16:00 Combining multiple packages +* 16:00 - 16:30 [Becoming a developer](package-development/README.md) in an open source project, best practices +* 16:30 - 17:00 Open slot, discussion, evaluation + +## Structure + +### Tool Foundations +Content relevant to each of the Open Radar packages (ex. Py-ART, wradlib, LROSE, BALTRAD). + +### Example Workflows +Workflows utilizing the various packages and open radar data. + +## Things You Need to Prepare +Participants need to bring their own 64-bit notebook (Linux, Windows, Mac). The exercices will take place on a cloud server. On Windows, the use of a ssh-client such as [Putty](https://www.putty.org/) or [MobaXterm](https://mobaxterm.mobatek.net/) will be necessary. diff --git a/_preview/104/_sources/baltrad/README.md b/_preview/104/_sources/baltrad/README.md new file mode 100644 index 00000000..7de953b9 --- /dev/null +++ b/_preview/104/_sources/baltrad/README.md @@ -0,0 +1,5 @@ +# BALTRAD Tutorial + +The BALTRAD approach will be introduced by outlining the ways in which it is commonly deployed, and then the data representation model used by the software will be explained. + +Following this introduction, course participants will get the chance to aquaint themselves with the look and feel of the BALTRAD Toolbox by running through a few notebooks that address data representation, quality control, compositing, and other features. Interoperability, how two software packages can pass data to each other, will be demonstrated by chosing between notebooks that do this between BALTRAD and Py-ART or between BALTRAD and wradlib. diff --git a/_preview/104/_sources/introductions/getting-started.md b/_preview/104/_sources/introductions/getting-started.md new file mode 100644 index 00000000..cf3c312f --- /dev/null +++ b/_preview/104/_sources/introductions/getting-started.md @@ -0,0 +1,104 @@ +# Getting Started + +## About + +The repository was created from [a ProjectPythia cookbook template](https://github.com/ProjectPythiaCookbooks/cookbook-template). +This template brings with it all machinery to enable full featured GitHub workflows, including building a docker image, running and rendering +Jupyter Notebooks and compiling a website using Sphinx and the [JupyterBook](https://jupyterbook.org/intro.html) theme. + +## Customizing + +If there is a package missing for your use-case you can just add the package to [binder/environment.yml](https://github.com/openradar/erad2022/blob/main/binder/environment.yml) and activate a new build (see below). +Notebooks might be added to the [notebooks-folder](https://github.com/openradar/erad2022/tree/main/notebooks). + +## Build workflow + +We work on pre-building the environment and toolkit so you can deploy this on the platform of your choice! Making the Open Radar Science stack available across different environments is essential to ensure reproducibility. We take advantage of "containerization", which enables packaging all of these tools together so you can deploy it on the platform of your choice. In this case, we are deploying a software stack which includes a variety of different languages (ex. Python, C++, C) and deploy it on a cloud platform. We use Docker as our main containerization tool. + +### Build on GitHub - PullRequest + +1. [repo2docker-action](https://github.com/jupyterhub/repo2docker-action) is used to build the docker image + - docker layers will be cached from [ghcr.io](https://github.com/openradar/erad2022/pkgs/container/erad2022) + - image is not pushed, since GITHUB_TOKEN `write` is not available as per security policy +1. book is built in a second step using [docker-run-action](https://github.com/addnab/docker-run-action), zipped and uploaded as artifact +1. book is deployed to gh-pages, link is added to pr comment + +### Build on GitHub - Push + +1. repo2docker-action is used to build the docker image + - docker layers will be cached from [ghcr.io](https://github.com/openradar/erad2022/pkgs/container/erad2022) + - image is pushed to [ghcr.io](https://github.com/openradar/erad2022/pkgs/container/erad2022) +1. book is built in a second job directly inside [ghcr.io](https://github.com/openradar/erad2022/pkgs/container/erad2022), zipped and uploaded as artifact +1. book is deployed to [gh-pages](https://openradarscience.org/erad2022/) + +Depending on the changes in the repo the build times can be quite low as docker layer caching is facilitated. + +### Build & run locally + +If you want to build and run locally using `repo2docker` you would need to remove (temporarily) the `binder/Dockerfile`. Then you would need to invoke the build with: + +``` +$ repo2docker --appendix "`cat binder/appendix.txt`" . +``` + +This will build the docker image locally and fire up a container running jupyterlab. + +### Run locally + +If you want to just run locally using `repo2docker` you will just use the provided `binder/Dockerfile`. Then you would need to invoke the run with: + +``` +$ repo2docker . +``` + +This will fetch the docker image from ghcr and fire up a container running jupyterlab. + +### Health check + +The complete build workflow is run as nightly build in GitHub CI to early detect problems. The Github action builds the environment, executes the notebooks, and checks the different links within the content. This ensures that the content is still executable, and we do not run into issues when building the environment or running the computational workflows. + +Running these health checks results in the following badge: +[![nightly-build](https://github.com/openradar/erad2022/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/openradar/erad2022/actions/workflows/nightly-build.yaml) + +Which is included in our README, letting users know whether the content is still executable. If this workflow fails, it also notifies the developers of this repository that there is an issue. + +### Conclusion + +- Docker images are built using GHA with all essential components inside (and pushed to ghcr.io on push) +- On binder we are using now our prebuild images on [ghcr.io](https://github.com/openradar/erad2022/pkgs/container/erad2022). As we are using `appendix` instead of `postBuild` no compilations are necessary on the binder side, the images is used as is with some minor adaptions by binder. +- On GHA we are using our prebuild images as layer cache, but layers can't be updated on pull requests due to security reasons. +- The erad2022 package on [ghcr.io](https://ghcr.io/openradar/erad2022:latest) represents always the status of the most recent commit to the repo. +- Running locally as well as building & running is easily possible. + + +## Using the PANGEO Cloud with Binder + +### 1. Make Sure You Have a Github Account +The first step is to make sure you have a Github Account. + +Here is the link if you do have one already: +- [Github account creation link](https://github.com/join) + +### 2. Log into Pangeo Binder +Next, sign in and authenticate the Pangeo Binder, which is the platform we will use for the workshop: +- [Pythia Binder Link](http://binder.projectpythia.org) + +The JupyterHub instance we use for this course is relatively small in memory and compute power (~10 GB of memory, 2 CPU cores). For more information about all of the open computational resrouces available within the Pangeo community, check out the [Pangeo Cloud](https://pangeo.io/cloud.html) documentation. + +### 3. Launch our Environment +Now that we have our authentication set up, we can access our content! + +Use the following link to launch into the binder: +- [Binder Link](http://binder.projectpythia.org/v2/gh/openradar/erad2022/main?labpath=notebooks) + +If you are having issues with that (ex. it is taking a long time), try using the following link: +``` +https://hub.aws-uswest2-binder.pangeo.io/user/{your_github_username}/lab +``` +Where you replace `{your_github_username}` with your github username (ex. `mgrover1`) + +## Running notebooks + +If you finally succeeded with the above procedure you will have a JupyterLab instance up and running. +Then you can select the notebooks as usual from the right navigation and simply run through them. +If you encounter any problems please [raise an issue here](https://github.com/openradar/erad2022/issues). diff --git a/_preview/104/_sources/introductions/open-radar.md b/_preview/104/_sources/introductions/open-radar.md new file mode 100644 index 00000000..5cc2696e --- /dev/null +++ b/_preview/104/_sources/introductions/open-radar.md @@ -0,0 +1,4 @@ +# Open Radar Community + +An overview of the open radar science community. + diff --git a/_preview/104/_sources/introductions/open-science.md b/_preview/104/_sources/introductions/open-science.md new file mode 100644 index 00000000..c0516a05 --- /dev/null +++ b/_preview/104/_sources/introductions/open-science.md @@ -0,0 +1,3 @@ +# An Overview of Open Science + +Open science introduction. \ No newline at end of file diff --git a/_preview/104/_sources/lrose/README.md b/_preview/104/_sources/lrose/README.md new file mode 100644 index 00000000..97d0e052 --- /dev/null +++ b/_preview/104/_sources/lrose/README.md @@ -0,0 +1,3 @@ +# LROSE Tutorial + +An overview and introduction to LROSE. \ No newline at end of file diff --git a/_preview/104/_sources/notebooks/baltrad2wradlib/README.md b/_preview/104/_sources/notebooks/baltrad2wradlib/README.md new file mode 100644 index 00000000..9f5ae418 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad2wradlib/README.md @@ -0,0 +1,6 @@ +baltrad2wradlib +=============== + +This is the interoperability demonstration between BALTRAD and wradlib. + +It contains the input ODIM_H5 file in folder ``/in`` and a notebook for the exercise. diff --git a/_preview/104/_sources/notebooks/baltrad2wradlib/baltrad2wradlib.ipynb b/_preview/104/_sources/notebooks/baltrad2wradlib/baltrad2wradlib.ipynb new file mode 100644 index 00000000..8f3c1925 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad2wradlib/baltrad2wradlib.ipynb @@ -0,0 +1,434 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Interaction of BALTRAD and wradlib via ODIM_H5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare your environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import Polygon\n", + "from matplotlib.collections import PatchCollection\n", + "import matplotlib.ticker as mticker\n", + "import wradlib\n", + "import shapefile\n", + "import gc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run BALTRAD's odc_toolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, you will process a scan from Suergavere (Estland) by using BALTRAD's odc_toolbox.\n", + "\n", + "From your VM's vagrant directory, navigate to the folder ``/baltrad2wradlib``.\n", + "\n", + "Execute the following command:\n", + "\n", + "``$ odc_toolbox -i in -o out -q ropo,radvol-att``\n", + "\n", + "Check whether a file was created in the folder ``/out``.\n", + "\n", + "**BALTRAD will not create output files if these already exist.** You can check that via ``!ls out``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!odc_toolbox -i in -o out -q ropo,radvol-att" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read and inspect data from Suergavere (Estonia) before and after QC with odc_toolbox" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Before QC\n", + "inp = wradlib.io.read_opera_hdf5(\"in/201405190715_SUR.h5\")\n", + "# After QC\n", + "out = wradlib.io.read_opera_hdf5(\"out/201405190715_SUR.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Here you can inspect whichever directory you want, e.g.\n", + "print(\"where...\\n\", inp['where'],\"\\n\")\n", + "print(\"what...\\n\", inp['what'],\"\\n\")\n", + "print(\"dataset1/data1/what...\\n\", inp['dataset1/data1/what'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert selected fields to target units" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def convert(dset, dir):\n", + " \"\"\"Converts ODIM_H5 data representations to values in target unit and masks missing values.\n", + " \n", + " Parameters\n", + " ----------\n", + " dset : the hdf5 object returned by wradlib.read_OPERA_hdf5\n", + " dir : the corresponding directory tree in dset for which the data should be processed\n", + " \n", + " \"\"\"\n", + " res = dset[dir+\"/data\"].astype(np.uint16)\n", + " try:\n", + " res = res * dset[dir+\"/what\"][\"gain\"] + dset[dir+\"/what\"][\"offset\"]\n", + " except:\n", + " pass\n", + " try:\n", + " res[dset[dir+\"/data\"]==dset[dir+\"/what\"][\"nodata\"]] = np.nan\n", + " except:\n", + " pass\n", + " try:\n", + " res[dset[dir+\"/data\"]==dset[dir+\"/what\"][\"undetect\"]] = np.nan\n", + " except:\n", + " pass \n", + "\n", + " return res " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract specific data arrays from ODIM_h5 objects\n", + "# dBZH before QC\n", + "before= convert(inp, \"dataset1/data1\")\n", + "# dBZH after QC\n", + "after = convert(out, \"dataset1/data1\")\n", + "# Quality field 1\n", + "qual1 = convert(out, \"dataset1/quality1\")\n", + "# Quality field 2\n", + "qual2 = convert(out, \"dataset1/quality2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect all the information required for georeferencing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collect all the georeferencing information we need\n", + "# First gate\n", + "r0 = inp['dataset1/where'][\"rstart\"]*1000.\n", + "# Gate length\n", + "rscale = inp['dataset1/where'][\"rscale\"]\n", + "# Number of bins per beam\n", + "nbins = inp['dataset1/where'][\"nbins\"]\n", + "# Maximum range\n", + "maxr = r0 + (nbins)*rscale\n", + "# Construct array of range gates\n", + "r = np.linspace(r0, maxr, nbins)\n", + "# Construct array of azimuth angles (quick and dirty)\n", + "az = np.linspace(0, 359, inp['dataset1/where'][\"nrays\"])\n", + "# Site coordinates (lon,lat)\n", + "site = (inp[\"where\"][\"lon\"], inp[\"where\"][\"lat\"], inp[\"where\"][\"height\"])\n", + "# Define a projection (Azimuthal Equidistant)\n", + "proj = wradlib.georef.create_osr(\"aeqd\", lon_0=site[0], lat_0=site[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Design a plot we will use for all PPIs in this exercise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A little helper funciton to harmonize all plots\n", + "def plot_ppi_to_ax(ppi, ax, title=\"\", cb=True, cb_label=\"\", cb_shrink=0.75, bbox=[-maxr, maxr, -maxr, maxr], extend=\"min\", **kwargs):\n", + " \"\"\"This is the function that we use in this exercise to plot PPIs with uniform georeferencing and style.\n", + " \"\"\"\n", + " # Read, project and plot country shapefile as background\n", + " # (got that snippet from http://stackoverflow.com/questions/15968762/shapefile-and-matplotlib-plot-polygon-collection-of-shapefile-coordinates)\n", + " sf = shapefile.Reader(\"shp/europe_countries.shp\", encoding=\"latin1\")\n", + " recs = sf.records()\n", + " shapes = sf.shapes()\n", + " Nshp = len(shapes)\n", + " # Iterate over shapes and create matplotlib Polygons\n", + " for nshp in range(Nshp):\n", + " ptchs = []\n", + " pts = np.array(shapes[nshp].points)\n", + " pts = wradlib.georef.reproject(pts, projection_target=proj)\n", + " prt = shapes[nshp].parts\n", + " par = list(prt) + [pts.shape[0]]\n", + " for pij in range(len(prt)):\n", + " ptchs.append(Polygon(pts[par[pij]:par[pij+1]]))\n", + " ax.add_collection(PatchCollection(ptchs,facecolor=\"lightgrey\",edgecolor='k', linewidths=1, zorder=-1))\n", + "\n", + "\n", + " # use wradlib.vis.plot_ppi\n", + " ax, pm = wradlib.vis.plot_ppi(np.ma.masked_invalid(ppi), ax=ax, r=r, az=az, site=site, proj=proj, **kwargs)\n", + " # and plot a cross hair\n", + " ax = wradlib.vis.plot_ppi_crosshair(site=site, ranges=[50000, 100000,150000, 200000, maxr], angles=[0, 90, 180, 270], \n", + " proj=proj, elev=0.5, ax=ax)\n", + " ##plt.text(x=-244000, y=-60000,s=\"250km\", color=\"gray\", rotation=-69)\n", + " ##plt.text(x=-192000, y=-60000,s=\"200km\", color=\"gray\", rotation=-67)\n", + " ##plt.text(x=-138000, y=-60000,s=\"150km\", color=\"gray\", rotation=-65)\n", + " \n", + " \n", + " # Do our final decorations\n", + " plt.xlim(bbox[0], bbox[1])\n", + " plt.ylim(bbox[2], bbox[3])\n", + " # set title\n", + " plt.title(title)\n", + " # convert axes unit from meters to kilometers\n", + " xticks_loc = ax.get_xticks().tolist()\n", + " yticks_loc = ax.get_yticks().tolist()\n", + " ax.xaxis.set_major_locator(mticker.FixedLocator(xticks_loc))\n", + " ax.yaxis.set_major_locator(mticker.FixedLocator(yticks_loc))\n", + " ax.set_xticklabels(['%g' % (0.001*l) for l in ax.get_xticks()])\n", + " ax.set_yticklabels(['%g' % (0.001*l) for l in ax.get_yticks()])\n", + " # set axes lables\n", + " plt.xlabel(\"x coordinates (km)\", fontsize=\"large\")\n", + " plt.ylabel(\"y coordinates (km)\", fontsize=\"large\")\n", + " # plot colorbar \n", + " if cb:\n", + " cbar = plt.colorbar(pm, shrink=cb_shrink, orientation=\"horizontal\", extend=extend, pad=0.1)\n", + " cbar.set_label(cb_label, fontsize=\"large\")\n", + " \n", + " gc.collect()\n", + " \n", + " return ax, pm\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the selected fields into one figure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12,10))\n", + "\n", + "ax = plt.subplot(221, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(before, ax=ax, title=\"Before QC\", cb=False, vmin=-10, vmax=65)\n", + "\n", + "ax = plt.subplot(222, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(after, ax=ax, title=\"After QC\", cb=False, vmin=-10, vmax=65)\n", + "\n", + "ax = plt.subplot(223, aspect=\"equal\")\n", + "ax, qm = plot_ppi_to_ax(qual1, ax=ax, title=\"Quality 1\", cb=False)\n", + "\n", + "ax = plt.subplot(224, aspect=\"equal\")\n", + "ax, qm = plot_ppi_to_ax(qual2, ax=ax, title=\"Quality 2\", cb=False)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "# Add colorbars\n", + "fig.subplots_adjust(right=0.9)\n", + "cax = fig.add_axes((0.9, 0.6, 0.03, 0.3))\n", + "cbar = plt.colorbar(pm, cax=cax)\n", + "cbar.set_label(\"Horizontal reflectivity (dBZ)\", fontsize=\"large\")\n", + "\n", + "cax = fig.add_axes((0.9, 0.1, 0.03, 0.3))\n", + "cbar = plt.colorbar(qm, cax=cax)\n", + "cbar.set_label(\"Quality index\", fontsize=\"large\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect and plot the polarimetric moments from the original ODIM_H5 dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We organise the moments as a dictionary\n", + "moments = {}\n", + "moments[\"rho\"] = convert(inp, \"dataset1/data2\") # RhoHV\n", + "moments[\"phi\"] = convert(inp, \"dataset1/data4\") # PhiDP\n", + "moments[\"zdr\"] = convert(inp, \"dataset1/data5\") # ZDR - the value range is not plausible, is it? What went wrong?\n", + "moments[\"dop\"] = convert(inp, \"dataset1/data10\") # Doppler velocity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12,12))\n", + "\n", + "ax = plt.subplot(221, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(moments[\"rho\"], ax=ax, title=\"RhoHV\", cb_label=\"(-)\", cb_shrink=0.6, extend=\"neither\")\n", + "\n", + "ax = plt.subplot(222, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(moments[\"phi\"], ax=ax, title=\"PhiDP\", cb_label=\"degree\", cb_shrink=0.6, extend=\"neither\")\n", + "\n", + "ax = plt.subplot(223, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(moments[\"zdr\"], ax=ax, title=\"Differential reflectivity\", cb_label=\"dB\", cb_shrink=0.6, extend=\"neither\")\n", + "\n", + "ax = plt.subplot(224, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(moments[\"dop\"], ax=ax, title=\"Doppler velocity\", cb_label=\"m/s\", cb_shrink=0.6, extend=\"neither\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try some filtering and attenuation correction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set ZH to a very low value where we do not expect valid data\n", + "zh_filtered = np.where(np.isnan(before), -32., before)\n", + "# Retrieve PIA by using some constraints (see http://wradlib.bitbucket.org/atten.html for help)\n", + "pia = wradlib.atten.correct_attenuation_constrained(zh_filtered,\n", + " constraints=[wradlib.atten.constraint_dbz,\n", + " wradlib.atten.constraint_pia],\n", + " constraint_args=[[64.0],\n", + " [20.0]])\n", + "# Correct reflectivity by PIA\n", + "after2 = before + pia\n", + "# Mask out non-meteorological echoes\n", + "after2[np.isnan(before)] = np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare results against QC from odc_toolbox" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(18,10))\n", + "bbox = [-maxr-2000,-100000, -100000, 100000]\n", + "shrink = 0.8\n", + "\n", + "ax = plt.subplot(131, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(before, ax=ax, title=\"Before QC\", cb_label=\"Horizontal reflectivity (dBZ)\", \n", + " cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)\n", + "\n", + "\n", + "ax = plt.subplot(132, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(after, ax=ax, title=\"After QC using BALTRAD Toolbox\", cb_label=\"Horizontal reflectivity (dBZ)\", \n", + " cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)\n", + "\n", + "\n", + "ax = plt.subplot(133, aspect=\"equal\")\n", + "ax, pm = plot_ppi_to_ax(after2, ax=ax, title=\"After QC using wradlib\", cb_label=\"Horizontal reflectivity (dBZ)\", \n", + " cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD Compositing.ipynb b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD Compositing.ipynb new file mode 100644 index 00000000..95321c9b --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD Compositing.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compositing with BALTRAD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This exercise builds on output from the parallel processing exercise. It does not address how projections and navigation is dealt with in BALTRAD. This should be addressed in a separate exercise.\n", + "\n", + "The Cartesian product area used in this exercise is pre-configured and looked up from a registry." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rudimentary composite" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import glob, time\n", + "import matplotlib\n", + "import _raveio, _rave\n", + "import _pycomposite, compositing\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Suppress SyntaxWarning from Python2 code\n", + "\n", + "generator = compositing.compositing()\n", + "generator.filenames = glob.glob(\"data/se*.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run with all defaults to a pre-configured area that uses the Google Maps projection.\n", + "# First two arguments are product date and time. These are taken from the last input file if not specified.\n", + "before = time.time()\n", + "comp = generator.generate(None, None, area=\"swegmaps_2000\")\n", + "after = time.time()\n", + "\n", + "rio = _raveio.new()\n", + "rio.object = comp\n", + "rio.save(\"data/comp_pcappi1000m.h5\")\n", + "\n", + "print(\"Compositing took %3.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tweak the plotter from earlier exercises" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE\n", + "from GmapColorMap import dbzh as dbzp\n", + "\n", + "# Convert a 768-list palette to a matplotlib colorlist\n", + "def make_colorlist(pal):\n", + " colorlist = []\n", + " for i in range(0, len(pal), 3):\n", + " colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])\n", + " return colorlist\n", + "\n", + "# Convert lists to colormaps\n", + "dbzcl = make_colorlist(dbzp)\n", + "\n", + "# Then create a simple plotter\n", + "import matplotlib.pyplot as plt\n", + "StringType = type('')\n", + "def plot(data, colorlist=dbzcl, title=\"Composite\"):\n", + " mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))\n", + " figsize=(20,16)# if mini == 0 else (12,8)\n", + " fig = plt.figure(figsize=figsize)\n", + " plt.title(title)\n", + " clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)\n", + " plt.imshow(data, cmap=clist, clim=(0,255))\n", + " plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(comp.getParameter(\"DBZH\").getData(), title=\"Default composite: DBZH 1000 m Pseudo-CAPPI, nearest radar\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maximum reflectivity, lowest pixel, add QC chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator.product = _rave.Rave_ProductType_MAX\n", + "generator.selection_method = _pycomposite.SelectionMethod_HEIGHT\n", + "generator.detectors = [\"ropo\", \"beamb\", \"radvol-att\", \"radvol-broad\", \"rave-overshooting\", \"qi-total\"]\n", + "before = time.time()\n", + "comp = generator.generate(None, None, area=\"swegmaps_2000\")\n", + "after = time.time()\n", + "rio.object = comp\n", + "rio.save(\"data/comp_max.h5\")\n", + "print(\"Compositing took %3.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(comp.getParameter(\"DBZH\").getData(), title=\"Maximum reflectivity, lowest pixel\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot correspondong total quality index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbzh = comp.getParameter(\"DBZH\")\n", + "qitot = dbzh.getQualityFieldByHowTask(\"pl.imgw.quality.qi_total\")\n", + "plot(qitot.getData(), \"binary\", \"Total quality index\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now use \"total quality\" as the compositing criterion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator.qitotal_field = \"pl.imgw.quality.qi_total\"\n", + "before = time.time()\n", + "comp = generator.generate(None, None, area=\"swegmaps_2000\")\n", + "after = time.time()\n", + "rio.object = comp\n", + "rio.save(\"data/comp_qitotal.h5\")\n", + "print(\"Compositing took %3.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(comp.getParameter(\"DBZH\").getData(), title=\"Maximum reflectivity, quality-based\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(comp.getParameter(\"DBZH\").getQualityFieldByHowTask(\"pl.imgw.quality.qi_total\").getData(), \"binary\", \"Total quality index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD DRQC.ipynb b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD DRQC.ipynb new file mode 100644 index 00000000..b7e31ab2 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD DRQC.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan\n", + "## We will also visualize the data using some openly-available colour tables.\n", + "## This notebook was originally prepared using material subsequently published in https://doi.org/10.1002/met.1929" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import _raveio\n", + "import ropo_realtime, ec_drqc\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import GmapColorMap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Block of look-ups for display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PALETTE = {} # To be populated\n", + "\n", + "UNDETECT = {\"TH\":GmapColorMap.PUREWHITE,\n", + " \"DBZH\":GmapColorMap.PUREWHITE,\n", + " \"DR\":GmapColorMap.PUREWHITE,\n", + " \"VRADH\":GmapColorMap.GREY5,\n", + " \"RHOHV\":GmapColorMap.PUREWHITE,\n", + " \"ZDR\":GmapColorMap.PUREWHITE}\n", + "\n", + "NODATA = {\"TH\":GmapColorMap.WEBSAFEGREY,\n", + " \"DBZH\":GmapColorMap.WEBSAFEGREY,\n", + " \"DR\":GmapColorMap.WEBSAFEGREY,\n", + " \"VRADH\":GmapColorMap.GREY8,\n", + " \"RHOHV\":GmapColorMap.WEBSAFEGREY,\n", + " \"ZDR\":GmapColorMap.WEBSAFEGREY}\n", + "\n", + "LEGEND = {\"TH\":'Radar reflectivity factor (dBZ)',\n", + " \"DBZH\":'Radar reflectivity factor (dBZ)',\n", + " \"DR\":'Depolarization ratio (dB)',\n", + " \"VRADH\":'Radial wind velocity away from radar (m/s)',\n", + " \"RHOHV\":'Cross-polar correlation coefficient',\n", + " \"ZDR\":\"Differential reflectivity (dB)\"}\n", + "\n", + "TICKS = {\"TH\":range(-30,80,10),\n", + " \"DBZH\":range(-30,80,10),\n", + " \"ZDR\":range(-8,9,2),\n", + " \"RHOHV\":np.arange(0,11,1)/10.,\n", + " \"VRADH\":range(-48,56,8),\n", + " \"DR\":range(-36,3,3)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colormap loader and loads" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def loadPal(fstr, reverse=True):\n", + " fd = open(fstr)\n", + " LINES = fd.readlines()\n", + " fd.close()\n", + " pal = []\n", + " for line in LINES:\n", + " s = line.split()\n", + " if reverse: s.reverse()\n", + " for val in s:\n", + " pal.append(int(float(val)*255))\n", + " if reverse: pal.reverse()\n", + " return pal\n", + "\n", + "# Colour maps by Fabio Crameri, http://www.fabiocrameri.ch/colourmaps.php, a couple of them modified\n", + "PALETTE[\"DBZH\"] = loadPal(\"data/hawaii.txt\")\n", + "PALETTE[\"DR\"] = loadPal(\"data/moleron.txt\", False) # Modified oleron\n", + "PALETTE[\"ZDR\"] = loadPal(\"data/oleron.txt\", False)\n", + "PALETTE[\"RHOHV\"] = loadPal(\"data/mroma.txt\") # Modified roma\n", + "PALETTE[\"VRADH\"] = loadPal(\"data/vik.txt\", False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up the display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def display(obj):\n", + " fig = plt.figure()\n", + " default_size = fig.get_size_inches()\n", + " fig.set_size_inches((default_size[0]*2, default_size[1]*2))\n", + "\n", + " paramname = obj.getParameterNames()[0]\n", + " pal = PALETTE[paramname]\n", + " pal[0], pal[1], pal[2] = UNDETECT[paramname] # Special value - areas radiated but void of echo\n", + " pal[767], pal[766], pal[765] = NODATA[paramname] # Special value - areas unradiated\n", + " if paramname == \"VRADH\":\n", + " pal[379], pal[380], pal[381] = GmapColorMap.PUREWHITE # VRADH isodop\n", + " pal[382], pal[383], pal[384] = GmapColorMap.PUREWHITE # VRADH isodop\n", + " pal[385], pal[386], pal[387] = GmapColorMap.PUREWHITE # VRADH isodop\n", + " colorlist = []\n", + " for i in range(0, len(pal), 3):\n", + " colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])\n", + "\n", + " param = obj.getParameter(paramname)\n", + " data = param.getData()\n", + " data = data*param.gain + param.offset\n", + " \n", + " im = plt.imshow(data,cmap=matplotlib.colors.ListedColormap(colorlist))\n", + " cax = plt.gca()\n", + " cax.axes.get_xaxis().set_visible(False)\n", + " cax.axes.get_yaxis().set_visible(False)\n", + "\n", + " cb = plt.colorbar(ticks=TICKS[paramname])\n", + " cb.set_label(LEGEND[paramname])\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Do the science\n", + "### Read the polar volume, QC the reflectivity using legacy ROPO, and then save the QC:ed result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.16.h5')\n", + "rio.object = ropo_realtime.generate(rio.object)\n", + "rio.save('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.ropo.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Re-read the polar volume, QC it using depolarization ratio, and then save the QC:ed result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.16.h5')\n", + "pvol = rio.object\n", + "ec_drqc.drQC(pvol)\n", + "rio.object = pvol\n", + "rio.save('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.drqc.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NOW LEAVE THIS NOTEBOOK AND GO TO A TERMINAL to generate CAPPIs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read and display CAPPIs, starting with Doppler-corrected reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_DBZH.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differential reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_ZDR.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cross-polar correlation coefficient" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_RHOHV.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Radial wind velocity, lowest PPI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ppi = _raveio.open('data/ppi_VRADH.h5').object\n", + "display(ppi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Depolarization ratio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_DR.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROPO:ed reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_DBZH_ropo.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DRQC:ed reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cappi = _raveio.open('data/cappi_DBZH_drqc.h5').object\n", + "display(cappi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD IO.ipynb b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD IO.ipynb new file mode 100644 index 00000000..ece2f664 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD IO.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BALTRAD I/O model - making sense out of data and metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the file I/O module along with the main RAVE module containing useful constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import _raveio, _rave" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read an input ODIM_H5 file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open(\"data/201405190715_SUR.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the payload in the I/O container?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio.objectType is _rave.Rave_ObjectType_PVOL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How many scans does this volume contain?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pvol = rio.object\n", + "print(\"%i scans in polar volume\" % pvol.getNumberOfScans())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ascending or descending scan strategy?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pvol.isAscendingScans()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Where is this site?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note that all angles are represented internally in radians" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Proj import rd\n", + "print(\"Site is located at %2.3f° lon, %2.3f° lat and %3.1f masl\" % (pvol.longitude*rd, pvol.latitude*rd, pvol.height))\n", + "print(\"Site's ODIM source identifiers are: %s\" % pvol.source)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access lowest scan and query some characteristics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan = pvol.getScan(0)\n", + "nrays, nbins = scan.nrays, scan.nbins\n", + "print(\"Elevation angle %2.1f°\" % (scan.elangle*rd))\n", + "print(\"%i rays per sweep\" % nrays)\n", + "print(\"%i bins per ray\" % nbins)\n", + "print(\"%3.1f meter range bins\" % scan.rscale)\n", + "print(\"First ray scanned is ray %i (indexing starts at 0)\" % scan.a1gate)\n", + "print(\"Data acquisition started on %s:%sZ\" % (scan.startdate, scan.starttime))\n", + "print(\"Data acquisition ended on %s:%sZ\" % (scan.enddate, scan.endtime))\n", + "print(\"Scan contains %i quantities: %s\" % (len(scan.getParameterNames()), scan.getParameterNames()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access horizontal reflectivity and query some characteristics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbzh = scan.getParameter(\"DBZH\")\n", + "print(\"Quantity is %s\" % dbzh.quantity)\n", + "print(\"8-bit unsigned byte data? %s\" % str(dbzh.datatype is _rave.RaveDataType_UCHAR))\n", + "print(\"Linear scaling coefficients from 0-255 to dBZ: gain=%2.1f, offset=%2.1f\" % (dbzh.gain, dbzh.offset))\n", + "print(\"Unradiated areas = %2.1f, radiated areas with no echo = %2.1f\" % (dbzh.nodata, dbzh.undetect))\n", + "\n", + "dbzh_data = dbzh.getData() # Accesses the NumPy array containing the reflectivities\n", + "print(\"NumPy array's dimensions = %s and type = %s\" % (str(dbzh_data.shape), dbzh_data.dtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A primitive visualizer for plotting B-scans" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convenience functionality. First convert a palette from GoogleMapsPlugin for use with matplotlib\n", + "import matplotlib\n", + "from GmapColorMap import dbzh as pal\n", + "colorlist = []\n", + "for i in range(0, len(pal), 3):\n", + " colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])\n", + "\n", + "# Then create a simple plotter\n", + "import matplotlib.pyplot as plt\n", + "def plot(data):\n", + " fig = plt.figure(figsize=(16,12))\n", + " plt.title(\"B-scan\")\n", + " plt.imshow(data, cmap=matplotlib.colors.ListedColormap(colorlist), clim=(0,255))\n", + " plt.colorbar(shrink=float(nrays)/nbins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(dbzh_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Management of optional metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### While manadatory metadata are represented as object attributes in Python, optional metadata are not!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Polar volume has %i optional attributes\" % len(pvol.getAttributeNames()))\n", + "print(\"Polar scan has %i optional attributes\" % len(scan.getAttributeNames()))\n", + "print(\"Quantity %s has %i optional attributes\" % (dbzh.quantity, len(dbzh.getAttributeNames())))\n", + "\n", + "print(\"Mandatory attribute: beamwidth is %2.1f°\" % (pvol.beamwidth*rd))\n", + "print(\"Optional attributes: Radar is a %s running %s\" % (pvol.getAttribute(\"how/system\"), pvol.getAttribute(\"how/software\")))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add a bogus attribute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbzh.foo = \"bar\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dbzh.addAttribute(\"how/foo\", \"bar\")\n", + "print(\"Quantity %s now has %i optional attributes\" % (dbzh.quantity, len(dbzh.getAttributeNames())))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an empty parameter and populate it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import _polarscanparam\n", + "param = _polarscanparam.new()\n", + "param.quantity = \"DBZH\"\n", + "param.nodata, param.undetect = 255.0, 0.0\n", + "param.gain, param.offset = 0.4, -30.0\n", + "\n", + "import numpy\n", + "data = numpy.zeros((420,500), numpy.uint8)\n", + "param.setData(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an empty scan and add the parameter to it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import _polarscan\n", + "from Proj import dr\n", + "newscan = _polarscan.new()\n", + "newscan.elangle = 25.0*dr\n", + "newscan.addAttribute(\"how/simulated\", \"True\")\n", + "\n", + "newscan.addParameter(param)\n", + "print(\"%i rays per sweep\" % newscan.nrays)\n", + "print(\"%i bins per ray\" % newscan.nbins)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### See how the parameter's dimensions were passed along to the scan, so they don't have to be set explicitly. Nevertheless, plenty of metadata must be handled explicitly or ODIM_H5 files risk being incomplete." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "newscan.a1gate = 0\n", + "newscan.beamwidth = 1.0*dr\n", + "newscan.rscale = 500.0\n", + "newscan.rstart = 0.0 # Distance in meters to the start of the first range bin, unknown=0.0\n", + "newscan.startdate = \"20140831\"\n", + "newscan.starttime = \"145005\"\n", + "newscan.enddate = \"20140831\"\n", + "newscan.endtime = \"145020\"\n", + "\n", + "# Top-level attributes\n", + "newscan.date = \"20140831\"\n", + "newscan.time = \"145000\"\n", + "newscan.source = \"WMO:26232,RAD:EE41,PLC:Sürgavere,NOD:eesur\"\n", + "newscan.longitude = 25.519*dr\n", + "newscan.latitude = 58.482*dr\n", + "newscan.height = 157.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now create a new I/O container and write the scan to ODIM_H5 file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "container = _raveio.new()\n", + "container.object = newscan\n", + "container.save(\"data/myscan.h5\")\n", + "\n", + "import os\n", + "print(\"ODIM_H5 file is %i bytes large\" % os.path.getsize(\"data/myscan.h5\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove compression. It makes file I/O faster. You can also tune HDF5 file-creation properties through the I/O container object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "container.compression_level = 0 # ZLIB compression levels 0-9\n", + "container.save(\"data/myscan.h5\")\n", + "print(\"ODIM_H5 file is now %i bytes large\" % os.path.getsize(\"data/myscan.h5\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD QC.ipynb b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD QC.ipynb new file mode 100644 index 00000000..ec0b4fa9 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD QC.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BALTRAD Quality Control" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the file I/O module along with the main RAVE module containing useful constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import _raveio, _rave" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read an input ODIM_H5 file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open(\"data/201405190715_SUR.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a simple plotter for B-scans, elaborating the example from the I/O exercise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE\n", + "from GmapColorMap import dbzh as dbzp\n", + "from rave_win_colors import continuous_MS as vradp\n", + "\n", + "# Convert a 768-list palette to a matplotlib colorlist\n", + "def make_colorlist(pal):\n", + " colorlist = []\n", + " for i in range(0, len(pal), 3):\n", + " colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])\n", + " return colorlist\n", + "\n", + "# Convert lists to colormaps\n", + "dbzcl = make_colorlist(dbzp)\n", + "vradcl = make_colorlist(vradp)\n", + "\n", + "# Then create a simple plotter\n", + "import matplotlib.pyplot as plt\n", + "#from types import StringType\n", + "StringType = type('')\n", + "def plot(data, colorlist=dbzcl, title=\"B-scan\"):\n", + " mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))\n", + " figsize=(16,12) if mini == 0 else (12,8)\n", + " fig = plt.figure(figsize=figsize)\n", + " plt.title(title)\n", + " clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)\n", + " plt.imshow(data, cmap=clist, clim=(0,255))\n", + " plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access the polar volume and plot VRAD data from the lowest scan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pvol = rio.object\n", + "plot(pvol.getScan(0).getParameter(\"VRADH\").getData(), vradcl, \"Original VRAD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dealias the volume" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import _dealias\n", + "ret = _dealias.dealias(pvol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check whether the first scan's been dealiased" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"This first scan is dealiased: %s\" % str(_dealias.dealiased(pvol.getScan(0))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replot for comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(pvol.getScan(0).getParameter(\"VRADH\").getData(), vradcl, \"Dealiased VRAD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shift gears - back to reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open(\"data/plrze_pvol_20120205T0430Z.h5\")\n", + "pvol = rio.object\n", + "plot(pvol.getScan(0).getParameter(\"DBZH\").getData(), title=\"Original DBZH\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use the bRopo package's quality plugin to identify and remove non-precipitation echoes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import odc_polarQC\n", + "import warnings\n", + "warnings.filterwarnings('ignore') # Suppress SyntaxWarning from Python2 code\n", + "\n", + "odc_polarQC.algorithm_ids = [\"ropo\"]\n", + "pvol = odc_polarQC.QC(pvol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the resulting DBZH" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(pvol.getScan(0).getParameter(\"DBZH\").getData(), title=\"DBZH after bRopo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Topographical beam-blockage QC using the beamb package's quality plugin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "odc_polarQC.algorithm_ids = [\"beamb\"]\n", + "before = time.time()\n", + "pvol = odc_polarQC.QC(pvol)\n", + "after = time.time()\n", + "print(\"beamb runtime = %2.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Probability of overshooting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "odc_polarQC.algorithm_ids = [\"rave-overshooting\"]\n", + "pvol = odc_polarQC.QC(pvol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing and manging data quality fields" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan = pvol.getScan(0)\n", + "print(\"Scan contains %i quality fields\" % scan.getNumberOfQualityFields())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(scan.getNumberOfQualityFields()):\n", + " qf = scan.getQualityField(i)\n", + " print(\"Quality field %i has identifier %s\" % (i, qf.getAttribute(\"how/task\")))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot quality fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Beam blockage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bb = scan.getQualityFieldByHowTask(\"se.smhi.detector.beamblockage\")\n", + "plot(bb.getData(), \"binary\", \"Quality indicator for beam blockage\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Probability of non-precipitation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bb = scan.getQualityFieldByHowTask(\"fi.fmi.ropo.detector.classification\")\n", + "plot(bb.getData(), \"binary\", \"Quality indicator for ropo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Probability of overshooting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bb = scan.getQualityFieldByHowTask(\"se.smhi.detector.poo\")\n", + "plot(bb.getData(), \"binary\", \"Quality indicator for PoO\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chaining algorithms - new data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open(\"data/sekir.h5\")\n", + "pvol = rio.object\n", + "\n", + "odc_polarQC.algorithm_ids = [\"ropo\", \"beamb\", \"radvol-att\", \"radvol-broad\", \"rave-overshooting\"]\n", + "pvol = odc_polarQC.QC(pvol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scan = pvol.getScan(0)\n", + "att = scan.getQualityField(2)\n", + "plot(att.getData(), \"binary\", \"Attenuation\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"Total Quality\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "odc_polarQC.algorithm_ids = [\"qi-total\"]\n", + "pvol = odc_polarQC.QC(pvol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qitot = scan.getQualityField(5)\n", + "plot(qitot.getData(), \"binary\", \"Total quality index\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD parallel processing.ipynb b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD parallel processing.ipynb new file mode 100644 index 00000000..28bcdab8 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/BALTRAD parallel processing.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BALTRAD parallel processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default VM setup is to use a single CPU core. In order to demonstrate the power of parallel processing, you must first determine whether your physical hardware has more than a single core.\n", + "\n", + "On Linux this is done in the terminal with the 'nproc' command.\n", + "\n", + "On Mac this is done in the terminal with the 'sysctl -n hw.ncpu' command.\n", + "\n", + "On Windows this is done graphically using the Task Manager's Performance tab.\n", + "\n", + "We want tune our VM to harness the power of several CPUs. Follow the following steps:\n", + "\n", + "1. Shut down the IPython notebook Server (Ctrl-C, answer yes)\n", + "2. Shutdown the VM (click the X button in the VM window, choose power down the machine)\n", + "3. Select the VM in the VirtualBox Manager Window, from the menu choose Machine->Setting\n", + "4. Choose the System Tab, then Processor, use the slider to set the number of Processor to 2, 4, or 8 depending on your system resources. \n", + "5. Click Ok, and then start the machine\n", + "6. Login, use the start_notebook.sh script to start the IPython server, start the notebook and you should have multiple processors!\n", + "\n", + "RELOAD THIS PAGE!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verify from Python the number of CPU cores at our disposal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import multiprocessing\n", + "print(\"We have %i cores to play with!\" % multiprocessing.cpu_count())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yay! Now we're going to set up some rudimentary functionality that will allow us to distribute a processing load among our cores." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define a generator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import _raveio, odc_polarQC\n", + "\n", + "# Specify the processing chain\n", + "odc_polarQC.algorithm_ids = [\"ropo\", \"beamb\", \"radvol-att\", \"radvol-broad\", \"rave-overshooting\", \"qi-total\"]\n", + "\n", + "# Run processing chain on a single file. Return an output file string.\n", + "def generate(file_string):\n", + " rio = _raveio.open(file_string)\n", + "\n", + " pvol = rio.object\n", + " pvol = odc_polarQC.QC(pvol)\n", + " rio.object = pvol\n", + " \n", + " # Derive an output file name\n", + " path, fstr = os.path.split(file_string)\n", + " ofstr = os.path.join(path, 'qc_'+fstr)\n", + " \n", + " rio.save(ofstr)\n", + " return ofstr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feed the generator, sequentially" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob, time\n", + "\n", + "ifstrs = glob.glob(\"data/se*.h5\")\n", + "before = time.time()\n", + "for fstr in ifstrs:\n", + " print(fstr, generate(fstr))\n", + "after = time.time()\n", + "\n", + "print(\"Processing time: %3.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mental note: repeat once!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiprocess the generator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Both input and output are a list of file strings\n", + "def multi_generate(fstrs, procs=None):\n", + " pool = multiprocessing.Pool(procs) # Pool of processors. Defaults to all available logical cores\n", + "\n", + " results = []\n", + " # chunksize=1 means feed a process a new job as soon as the process is idle.\n", + " # In our case, this restricts the queue to one \"dispatcher\" which is faster.\n", + " r = pool.map_async(generate, fstrs, chunksize=1, callback=results.append)\n", + " r.wait()\n", + "\n", + " return results[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feed the monster, asynchronously!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "before = time.time()\n", + "ofstrs = multi_generate(ifstrs)\n", + "after = time.time()\n", + "\n", + "print(\"Processing time: %3.2f seconds\" % (after-before))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/baltrad_short_course/README.md b/_preview/104/_sources/notebooks/baltrad_short_course/README.md new file mode 100644 index 00000000..ee957304 --- /dev/null +++ b/_preview/104/_sources/notebooks/baltrad_short_course/README.md @@ -0,0 +1,4 @@ +baltrad_short_course +==================== + +IPy Notebook exercises and data for the BALTRAD Toolbox diff --git a/_preview/104/_sources/notebooks/environment.ipynb b/_preview/104/_sources/notebooks/environment.ipynb new file mode 100644 index 00000000..026df619 --- /dev/null +++ b/_preview/104/_sources/notebooks/environment.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Environment overview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!env" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!conda list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import wradlib as wrl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wrl.show_versions()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_preview/104/_sources/notebooks/lrose/nexrad_mosaic.erad_tutorial.ipynb b/_preview/104/_sources/notebooks/lrose/nexrad_mosaic.erad_tutorial.ipynb new file mode 100644 index 00000000..a000812f --- /dev/null +++ b/_preview/104/_sources/notebooks/lrose/nexrad_mosaic.erad_tutorial.ipynb @@ -0,0 +1,7437 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ee9cfc3", + "metadata": {}, + "source": [ + "## ERAD 2022 Open Source Workshop\n", + "\n", + "### LROSE workflow - combining 3 NEXRAD radars, computing PID and Echo Type\n", + "\n", + "\n", + " \n", + "The case we will use is from 2021/07/06 22:00 UTC, when a series of MCSs passed through the NW region of Kansas in the USA.\n", + "\n", + "We will combine the reflectivity of 3 NEXRAD radars into a mini-mosaic:\n", + "\n", + "- KGLD - Goodland, Kansas\n", + "- KUEX - Hastings, Nebraska\n", + "- KDDC - Dodge City, Kansas\n", + "\n", + "\n", + "We will download the following data sets from the cloud:\n", + "\n", + "- raw NEXRAD files for KGLD, KUEX and KDDC, 2021/07/06 from 22:00 UTC to 22:30 UTC\n", + "- RUC model output for Kansas region, CF-NetCDF format, from 2021/07/06 at 23:00 UTC\n", + "\n", + "The model data will provide a temperature profile for computing PID and Echo Type.\n", + "\n", + "The workflow is as follows:\n", + "\n", + "- download the data into ```/tmp/lrose_data/nexrad_mosaic```.\n", + "- run LROSE app RadxConvert, to convert raw NEXRAD files to CfRadial.\n", + "- plot an example PPI from KGLD using PyArt.\n", + "- read in temperature data from RUC file, plot cross sections using Matplotlib.\n", + "- run the LROSE app Mdv2SoundingSpdb to derive the temperature profile for each radar site from the RUC data file, and store in SPDB (a simple time-indexed data base).\n", + "- run the LROSE app RadxRate to compute precipition rate and Particle ID (PID).\n", + "- plot KDP, PID and precipitation rate for an example PPI.\n", + "- run the LROSE app Radx2Grid to convert the polar CfRadial files into Cartesian coordinates.\n", + "- run the LROSE app MdvMerge2 to merge the Cartesian files from the 3 radars into a reflectivity mini-mosaic.\n", + "- plot selected views of the reflectivity mosaic, using Matplotlib.\n", + "- run the LROSE app Ecco to compute the convective/stratiform partition using the reflectivity mosaic and the RUC temperature profile.\n", + "- plot the results of Ecco using Matplotlib.\n", + "\n", + "We will use the following parameter files for the LROSE applications:\n", + "\n", + "- params/RadxConvert.nexrad - convert raw NEXRAD files to CfRadial.\n", + "- params/Mdv2SoundingSpdb.ruc - create temperature profiles for each radar from RUC model temperature.\n", + "- params/RadxRate.nexrad - computes KDP, PID and precipition rate.\n", + "- params/kdp_params.nexrad - used by RadxRate to compute KDP.\n", + "- params/pid_params.nexrad - used by RadxRate to compute PID.\n", + "- params/pid_thresholds.nexrad - used by RadxRate to compute PID.\n", + "- params/rate_params.nexrad - used by RadxRate to compute precipitation rate.\n", + "- params/Ecco.nexrad_mosaic - used by Ecco to compute echo type classifications.\n", + "\n", + "After the download step, the input files will be in:\n", + "\n", + "```\n", + " /tmp/lrose_data/nexrad_mosaic/raw/KGLD\n", + " /tmp/lrose_data/nexrad_mosaic/raw/KUEX\n", + " /tmp/lrose_data/nexrad_mosaic/raw/KDDC\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/ruc\n", + "```\n", + "\n", + "The output files will be stored in:\n", + "\n", + "```\n", + " /tmp/lrose_data/nexrad_mosaic/cfradial (polar data)\n", + " /tmp/lrose_data/nexrad_mosaic/mdv (Cartesian data)\n", + " /tmp/lrose_data/nexrad_mosaic/spdb (temperature profile per radar)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "52e9d69e", + "metadata": {}, + "source": [ + "### Initialize python" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "22982961", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====>> nexradDataDir: /tmp/lrose_data/nexrad_mosaic\n" + ] + } + ], + "source": [ + "#\n", + "# Extra packages to be added to anaconda3 standard packages for this notebook:\n", + "#\n", + "# conda update --all\n", + "# conda install cartopy netCDF4\n", + "# conda install -c conda-forge arm_pyart\n", + "#\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import os\n", + "import datetime\n", + "import pytz\n", + "import math\n", + "import numpy as np\n", + "import matplotlib as mp\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as plticker\n", + "from matplotlib.lines import Line2D\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.io.shapereader as shpreader\n", + "import cartopy.geodesic as cgds\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "from cartopy import feature as cfeature\n", + "import shapely\n", + "import netCDF4 as nc\n", + "import pyart\n", + "\n", + "# Set data dir in environment variable\n", + "os.environ['NEXRAD_DATA_DIR'] = '/tmp/lrose_data/nexrad_mosaic'\n", + "nexradDataDir = os.environ['NEXRAD_DATA_DIR']\n", + "print('====>> nexradDataDir: ', nexradDataDir)" + ] + }, + { + "cell_type": "markdown", + "id": "6de68023", + "metadata": {}, + "source": [ + "### Download data sets from web" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9f896c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====>> Downloading data tar files into dir: /tmp/lrose_data/nexrad_mosaic\n", + "--2022-08-23 17:16:34-- http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KGLD.20210706_220000.tgz\n", + "Resolving front.eol.ucar.edu (front.eol.ucar.edu)... 128.117.43.125\n", + "Connecting to front.eol.ucar.edu (front.eol.ucar.edu)|128.117.43.125|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 74762580 (71M) [application/x-gzip]\n", + "Saving to: 'nexrad_mosaic.KGLD.20210706_220000.tgz'\n", + "\n", + "nexrad_mosaic.KGLD. 100%[===================>] 71.30M 111MB/s in 0.6s \n", + "\n", + "2022-08-23 17:16:35 (111 MB/s) - 'nexrad_mosaic.KGLD.20210706_220000.tgz' saved [74762580/74762580]\n", + "\n", + "--2022-08-23 17:16:35-- http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KDDC.20210706_220000.tgz\n", + "Resolving front.eol.ucar.edu (front.eol.ucar.edu)... 128.117.43.125\n", + "Connecting to front.eol.ucar.edu (front.eol.ucar.edu)|128.117.43.125|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 69853288 (67M) [application/x-gzip]\n", + "Saving to: 'nexrad_mosaic.KDDC.20210706_220000.tgz'\n", + "\n", + "nexrad_mosaic.KDDC. 100%[===================>] 66.62M 109MB/s in 0.6s \n", + "\n", + "2022-08-23 17:16:35 (109 MB/s) - 'nexrad_mosaic.KDDC.20210706_220000.tgz' saved [69853288/69853288]\n", + "\n", + "--2022-08-23 17:16:36-- http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KUEX.20210706_220000.tgz\n", + "Resolving front.eol.ucar.edu (front.eol.ucar.edu)... 128.117.43.125\n", + "Connecting to front.eol.ucar.edu (front.eol.ucar.edu)|128.117.43.125|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 59062156 (56M) [application/x-gzip]\n", + "Saving to: 'nexrad_mosaic.KUEX.20210706_220000.tgz'\n", + "\n", + "nexrad_mosaic.KUEX. 100%[===================>] 56.33M 111MB/s in 0.5s \n", + "\n", + "2022-08-23 17:16:36 (111 MB/s) - 'nexrad_mosaic.KUEX.20210706_220000.tgz' saved [59062156/59062156]\n", + "\n", + "--2022-08-23 17:16:37-- http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz\n", + "Resolving front.eol.ucar.edu (front.eol.ucar.edu)... 128.117.43.125\n", + "Connecting to front.eol.ucar.edu (front.eol.ucar.edu)|128.117.43.125|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4039462 (3.9M) [application/x-gzip]\n", + "Saving to: 'nexrad_mosaic.ruc.20210706_220000.tgz'\n", + "\n", + "nexrad_mosaic.ruc.2 100%[===================>] 3.85M --.-KB/s in 0.06s \n", + "\n", + "2022-08-23 17:16:37 (68.3 MB/s) - 'nexrad_mosaic.ruc.20210706_220000.tgz' saved [4039462/4039462]\n", + "\n", + "====>> The following data files are unpacked in dir: /tmp/lrose_data/nexrad_mosaic\n", + "mdv/\n", + "mdv/ruc/\n", + "mdv/ruc/20210706/\n", + "mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "raw/KGLD/\n", + "raw/KGLD/20210706/\n", + "raw/KGLD/20210706/KGLD20210706_220003_V06\n", + "raw/KGLD/20210706/KGLD20210706_220448_V06\n", + "raw/KGLD/20210706/KGLD20210706_220935_V06\n", + "raw/KGLD/20210706/KGLD20210706_221420_V06\n", + "raw/KGLD/20210706/KGLD20210706_221906_V06\n", + "raw/KGLD/20210706/KGLD20210706_222350_V06\n", + "raw/KGLD/20210706/KGLD20210706_222834_V06\n", + "raw/KDDC/\n", + "raw/KDDC/20210706/\n", + "raw/KDDC/20210706/KDDC20210706_220000_V06\n", + "raw/KDDC/20210706/KDDC20210706_220430_V06\n", + "raw/KDDC/20210706/KDDC20210706_220921_V06\n", + "raw/KDDC/20210706/KDDC20210706_221600_V06\n", + "raw/KDDC/20210706/KDDC20210706_222051_V06\n", + "raw/KDDC/20210706/KDDC20210706_222533_V06\n", + "raw/KUEX/\n", + "raw/KUEX/20210706/\n", + "raw/KUEX/20210706/KUEX20210706_220249_V06\n", + "raw/KUEX/20210706/KUEX20210706_220723_V06\n", + "raw/KUEX/20210706/KUEX20210706_221204_V06\n", + "raw/KUEX/20210706/KUEX20210706_221633_V06\n", + "raw/KUEX/20210706/KUEX20210706_222102_V06\n", + "raw/KUEX/20210706/KUEX20210706_222531_V06\n" + ] + } + ], + "source": [ + "# Download input data sets\n", + "#\n", + "# 1. NEXRAD raw data files for KGLD, KDDC and KUEX\n", + "# 2. RUC model data for temperature profile, Kansas and surroundings\n", + "#\n", + "# These will be put in:\n", + "# ${NEXRAD_DATA_DIR}\n", + "#\n", + "\n", + "# ensure the data dir exists and is clean\n", + "\n", + "!/bin/rm -rf ${NEXRAD_DATA_DIR}\n", + "!mkdir -p ${NEXRAD_DATA_DIR}\n", + "\n", + "# download the data from github\n", + "\n", + "print(\"====>> Downloading data tar files into dir: \", nexradDataDir)\n", + "\n", + "!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KGLD.20210706_220000.tgz \n", + "!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KDDC.20210706_220000.tgz \n", + "!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KUEX.20210706_220000.tgz \n", + "!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz \n", + " \n", + "# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz\n", + "# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KGLD.20210706_220000.tgz\n", + "# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KDDC.20210706_220000.tgz\n", + "# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KUEX.20210706_220000.tgz\n", + "\n", + "# extract the data from the tar files\n", + "\n", + "print(\"====>> The following data files are unpacked in dir: \", nexradDataDir)\n", + "!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.ruc.20210706_220000.tgz\n", + "!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KGLD.20210706_220000.tgz\n", + "!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KDDC.20210706_220000.tgz\n", + "!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KUEX.20210706_220000.tgz\n", + "\n", + "# clean up\n", + "\n", + "!cd ${NEXRAD_DATA_DIR}; /bin/rm -f *tgz\n" + ] + }, + { + "cell_type": "markdown", + "id": "3511188c", + "metadata": {}, + "source": [ + "## Notes on LROSE Parameter files\n", + "\n", + "All LROSE applications have a detailed parameter file, which is read in at startup.\n", + "\n", + "The parameters allow the user to control the processing in the LROSE apps.\n", + "\n", + "To generate a default parameter file, you use the -print_params option for the app.\n", + "\n", + "For example, for RadxConvert you would use:\n", + "\n", + "```\n", + " RadxConvert -print_params > RadxConvert.nexrad\n", + "```\n", + "\n", + "and then edit RadxConvert.nexrad appropriately.\n", + "\n", + "At runtime you would use:\n", + "\n", + "```\n", + " RadxConvert -params RadxConvert.nexrad ... etc ...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "d34dfd14", + "metadata": {}, + "source": [ + "## View the RadxConvert parameter file\n", + "\n", + "Note that we can use environment variables in the parameter files.\n", + "\n", + "Environment variables are inserted using the format:\n", + "\n", + "```\n", + " $(env_var_name)\n", + "```\n", + "\n", + "For example:\n", + "\n", + "```\n", + " input_dir = \"$(NEXRAD_DATA_DIR)/raw/$(RADAR_NAME)\";\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9eccf394", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/**********************************************************************\r\n", + " * TDRP params for RadxConvert\r\n", + " **********************************************************************/\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// Converts files between CfRadial and other radial formats.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "//======================================================================\r\n", + "//\r\n", + "// DEBUGGING.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// debug ///////////////////////////////////\r\n", + "//\r\n", + "// Debug option.\r\n", + "// If set, debug messages will be printed appropriately.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// DEBUG_OFF\r\n", + "// DEBUG_NORM\r\n", + "// DEBUG_VERBOSE\r\n", + "// DEBUG_EXTRA\r\n", + "//\r\n", + "\r\n", + "debug = DEBUG_OFF;\r\n", + "\r\n", + "///////////// instance ////////////////////////////////\r\n", + "//\r\n", + "// Program instance for process registration.\r\n", + "// This application registers with procmap. This is the instance used \r\n", + "// for registration.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "instance = \"$(RADAR_NAME)\";\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// DATA INPUT.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// input_dir ///////////////////////////////\r\n", + "//\r\n", + "// Input directory for searching for files.\r\n", + "// Files will be searched for in this directory.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "input_dir = \"$(NEXRAD_DATA_DIR)/raw/$(RADAR_NAME)\";\r\n", + "\r\n", + "///////////// mode ////////////////////////////////////\r\n", + "//\r\n", + "// Operating mode.\r\n", + "// In REALTIME mode, the program waits for a new input file. In ARCHIVE \r\n", + "// mode, it moves through the data between the start and end times set \r\n", + "// on the command line. In FILELIST mode, it moves through the list of \r\n", + "// file names specified on the command line. Paths (in ARCHIVE mode, at \r\n", + "// least) MUST contain a day-directory above the data file -- \r\n", + "// ./data_file.ext will not work as a file path, but \r\n", + "// ./yyyymmdd/data_file.ext will.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// REALTIME\r\n", + "// ARCHIVE\r\n", + "// FILELIST\r\n", + "//\r\n", + "\r\n", + "mode = ARCHIVE;\r\n", + "\r\n", + "///////////// max_realtime_data_age_secs //////////////\r\n", + "//\r\n", + "// Maximum age of realtime data (secs).\r\n", + "// Only data less old than this will be used.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "max_realtime_data_age_secs = 300;\r\n", + "\r\n", + "///////////// latest_data_info_avail //////////////////\r\n", + "//\r\n", + "// Is _latest_data_info file available?.\r\n", + "// If TRUE, will watch the latest_data_info file. If FALSE, will scan \r\n", + "// the input directory for new files.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "latest_data_info_avail = FALSE;\r\n", + "\r\n", + "///////////// search_recursively //////////////////////\r\n", + "//\r\n", + "// Option to recurse to subdirectories while looking for new files.\r\n", + "// If TRUE, all subdirectories with ages less than max_dir_age will be \r\n", + "// searched. This may take considerable CPU, so be careful in its use. \r\n", + "// Only applies if latest_data_info_avail is FALSE.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "search_recursively = TRUE;\r\n", + "\r\n", + "///////////// max_recursion_depth /////////////////////\r\n", + "//\r\n", + "// Maximum depth for recursive directory scan.\r\n", + "// Only applies search_recursively is TRUE. This is the max depth, below \r\n", + "// input_dir, to which the recursive directory search will be carried \r\n", + "// out. A depth of 0 will search the top-level directory only. A depth \r\n", + "// of 1 will search the level below the top directory, etc.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "max_recursion_depth = 5;\r\n", + "\r\n", + "///////////// wait_between_checks /////////////////////\r\n", + "//\r\n", + "// Sleep time between checking directory for input - secs.\r\n", + "// If a directory is large and files do not arrive frequently, set this \r\n", + "// to a higher value to reduce the CPU load from checking the directory. \r\n", + "// Only applies if latest_data_info_avail is FALSE.\r\n", + "// Minimum val: 1\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "wait_between_checks = 2;\r\n", + "\r\n", + "///////////// file_quiescence /////////////////////////\r\n", + "//\r\n", + "// File quiescence when checking for files - secs.\r\n", + "// This allows you to make sure that a file coming from a remote machine \r\n", + "// is complete before reading it. Only applies if latest_data_info_avail \r\n", + "// is FALSE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "file_quiescence = 60;\r\n", + "\r\n", + "///////////// search_ext //////////////////////////////\r\n", + "//\r\n", + "// File name extension.\r\n", + "// If set, only files with this extension will be processed.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "search_ext = \"\";\r\n", + "\r\n", + "///////////// gematronik_realtime_mode ////////////////\r\n", + "//\r\n", + "// Set to TRUE if we are watching for Gematronik XML volumes.\r\n", + "// Gematronik volumes (for a given time) are stored in multiple files, \r\n", + "// one for each field. Therefore, after the time on a volume changes and \r\n", + "// a new field file is detected, we need to wait a while to ensure that \r\n", + "// all of the files have had a chance to be writted to disk. You need to \r\n", + "// set gematronik_realtime_wait_secs to a value in excess of the time it \r\n", + "// takes for all of the files to be written.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "gematronik_realtime_mode = FALSE;\r\n", + "\r\n", + "///////////// gematronik_realtime_wait_secs ///////////\r\n", + "//\r\n", + "// Number of seconds to wait, so that all field files can be written to \r\n", + "// disk before we start to read.\r\n", + "// See 'gematronik_realtime_mode'.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "gematronik_realtime_wait_secs = 5;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTIONAL FIXED ANGLE OR SWEEP NUMBER LIMITS.\r\n", + "//\r\n", + "// Fixed angles are elevation in PPI mode and azimuth in RHI mode.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// set_fixed_angle_limits //////////////////\r\n", + "//\r\n", + "// Option to set fixed angle limits.\r\n", + "// Only use sweeps within the specified fixed angle limits.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_fixed_angle_limits = FALSE;\r\n", + "\r\n", + "///////////// lower_fixed_angle_limit /////////////////\r\n", + "//\r\n", + "// Lower fixed angle limit - degrees.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "lower_fixed_angle_limit = 0;\r\n", + "\r\n", + "///////////// upper_fixed_angle_limit /////////////////\r\n", + "//\r\n", + "// Upper fixed angle limit - degrees.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "upper_fixed_angle_limit = 90;\r\n", + "\r\n", + "///////////// set_sweep_num_limits ////////////////////\r\n", + "//\r\n", + "// Option to set sweep number limits.\r\n", + "// If 'apply_strict_angle_limits' is set, only read sweeps within the \r\n", + "// specified limits. If strict checking is false and no data lies within \r\n", + "// the limits, return the closest applicable sweep.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_sweep_num_limits = FALSE;\r\n", + "\r\n", + "///////////// lower_sweep_num /////////////////////////\r\n", + "//\r\n", + "// Lower sweep number limit.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "lower_sweep_num = 0;\r\n", + "\r\n", + "///////////// upper_sweep_num /////////////////////////\r\n", + "//\r\n", + "// Upper sweep number limit.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "upper_sweep_num = 0;\r\n", + "\r\n", + "///////////// apply_strict_angle_limits ///////////////\r\n", + "//\r\n", + "// Option to apply strict checking for angle or sweep number limits on \r\n", + "// read.\r\n", + "// If true, an error will occur if the fixed angle limits or sweep num \r\n", + "// limits are outside the bounds of the data. If false, a read is \r\n", + "// guaranteed to return at least 1 sweep - if no sweep lies within the \r\n", + "// angle limits set, the nearest sweep will be returned.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_strict_angle_limits = TRUE;\r\n", + "\r\n", + "///////////// read_set_radar_num //////////////////////\r\n", + "//\r\n", + "// Option to set the radar number.\r\n", + "// See read_radar_num.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "read_set_radar_num = FALSE;\r\n", + "\r\n", + "///////////// read_radar_num //////////////////////////\r\n", + "//\r\n", + "// Set the radar number for the data to be extracted.\r\n", + "// Most files have data from a single radar, so this does not apply. The \r\n", + "// NOAA HRD files, however, have data from both the lower fuselage (LF, \r\n", + "// radar_num = 1) and tail (TA, radar_num = 2) radars. For HRD files, by \r\n", + "// default the TA radar will be used, unless the radar num is set to 1 \r\n", + "// for the LF radar.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "read_radar_num = 0;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// READ OPTIONS.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// aggregate_sweep_files_on_read ///////////\r\n", + "//\r\n", + "// Option to aggregate sweep files into a volume on read.\r\n", + "// If true, and the input data is in sweeps rather than volumes (e.g. \r\n", + "// DORADE), the sweep files from a volume will be aggregated into a \r\n", + "// volume.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "aggregate_sweep_files_on_read = FALSE;\r\n", + "\r\n", + "///////////// aggregate_all_files_on_read /////////////\r\n", + "//\r\n", + "// Option to aggregate all files in the file list on read.\r\n", + "// If true, all of the files specified with the '-f' arg will be \r\n", + "// aggregated into a single volume as they are read in. This only \r\n", + "// applies to FILELIST mode. Overrides 'aggregate_sweep_files_on_read'.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "aggregate_all_files_on_read = FALSE;\r\n", + "\r\n", + "///////////// ignore_idle_scan_mode_on_read ///////////\r\n", + "//\r\n", + "// Option to ignore data taken in IDLE mode.\r\n", + "// If true, on read will ignore files with an IDLE scan mode.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "ignore_idle_scan_mode_on_read = TRUE;\r\n", + "\r\n", + "///////////// remove_rays_with_all_data_missing ///////\r\n", + "//\r\n", + "// Option to remove rays for which all data is missing.\r\n", + "// If true, ray data will be checked. If all fields have missing data at \r\n", + "// all gates, the ray will be removed after reading.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "remove_rays_with_all_data_missing = FALSE;\r\n", + "\r\n", + "///////////// remove_rays_with_antenna_transitions ////\r\n", + "//\r\n", + "// Option to remove rays taken while the antenna was in transition.\r\n", + "// If true, rays with the transition flag set will not be used. The \r\n", + "// transiton flag is set when the antenna is in transtion between one \r\n", + "// sweep and the next.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "remove_rays_with_antenna_transitions = FALSE;\r\n", + "\r\n", + "///////////// transition_nrays_margin /////////////////\r\n", + "//\r\n", + "// Number of transition rays to include as a margin.\r\n", + "// Sometimes the transition flag is turned on too early in a transition, \r\n", + "// on not turned off quickly enough after a transition. If you set this \r\n", + "// to a number greater than 0, that number of rays will be included at \r\n", + "// each end of the transition, i.e. the transition will effectively be \r\n", + "// shorter at each end by this number of rays.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "transition_nrays_margin = 0;\r\n", + "\r\n", + "///////////// trim_surveillance_sweeps_to_360deg //////\r\n", + "//\r\n", + "// Option to trip surveillance sweeps so that they only cover 360 \r\n", + "// degrees.\r\n", + "// Some sweeps will have rays which cover more than a 360-degree \r\n", + "// rotation. Often these include antenna transitions. If this is set to \r\n", + "// true, rays are trimmed off either end of the sweep to limit the \r\n", + "// coverage to 360 degrees. The median elevation angle is computed and \r\n", + "// the end ray which deviates from the median in elevation is trimmed \r\n", + "// first.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "trim_surveillance_sweeps_to_360deg = FALSE;\r\n", + "\r\n", + "///////////// set_max_range ///////////////////////////\r\n", + "//\r\n", + "// Option to set the max range for any ray.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_max_range = TRUE;\r\n", + "\r\n", + "///////////// max_range_km ////////////////////////////\r\n", + "//\r\n", + "// Specified maximim range - km.\r\n", + "// Gates beyond this range are removed.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "max_range_km = 230.0;\r\n", + "\r\n", + "///////////// preserve_sweeps /////////////////////////\r\n", + "//\r\n", + "// Preserve sweeps just as they are in the file.\r\n", + "// Applies generally to NEXRAD data. If true, the sweep details are \r\n", + "// preserved. If false, we consolidate sweeps from split cuts into a \r\n", + "// single sweep.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "preserve_sweeps = FALSE;\r\n", + "\r\n", + "///////////// remove_long_range_rays //////////////////\r\n", + "//\r\n", + "// Option to remove long range rays.\r\n", + "// Applies to NEXRAD data. If true, data from the non-Doppler long-range \r\n", + "// sweeps will be removed.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "remove_long_range_rays = TRUE;\r\n", + "\r\n", + "///////////// remove_short_range_rays /////////////////\r\n", + "//\r\n", + "// Option to remove short range rays.\r\n", + "// Applies to NEXRAD data. If true, data from the Doppler short-range \r\n", + "// sweeps will be removed.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "remove_short_range_rays = FALSE;\r\n", + "\r\n", + "///////////// set_ngates_constant /////////////////////\r\n", + "//\r\n", + "// Option to force the number of gates to be constant.\r\n", + "// If TRUE, the number of gates on all rays will be set to the maximum, \r\n", + "// and gates added to shorter rays will be filled with missing values.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_ngates_constant = FALSE;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO OVERRIDE INSTRUMENT AND/OR NAME.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// override_instrument_name ////////////////\r\n", + "//\r\n", + "// Option to override the instrument name.\r\n", + "// If true, the name provided will be used.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "override_instrument_name = FALSE;\r\n", + "\r\n", + "///////////// instrument_name /////////////////////////\r\n", + "//\r\n", + "// Instrument name.\r\n", + "// See override_instrument_name.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "instrument_name = \"unknown\";\r\n", + "\r\n", + "///////////// override_site_name //////////////////////\r\n", + "//\r\n", + "// Option to override the site name.\r\n", + "// If true, the name provided will be used.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "override_site_name = FALSE;\r\n", + "\r\n", + "///////////// site_name ///////////////////////////////\r\n", + "//\r\n", + "// Site name.\r\n", + "// See override_site_name.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "site_name = \"unknown\";\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO OVERRIDE RADAR LOCATION.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// override_radar_location /////////////////\r\n", + "//\r\n", + "// Option to override the radar location.\r\n", + "// If true, the location in this file will be used. If not, the location \r\n", + "// in the time series data will be used.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "override_radar_location = FALSE;\r\n", + "\r\n", + "///////////// radar_latitude_deg //////////////////////\r\n", + "//\r\n", + "// Radar latitude (deg).\r\n", + "// See override_radar_location.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "radar_latitude_deg = -999;\r\n", + "\r\n", + "///////////// radar_longitude_deg /////////////////////\r\n", + "//\r\n", + "// Radar longitude (deg).\r\n", + "// See override_radar_location.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "radar_longitude_deg = -999;\r\n", + "\r\n", + "///////////// radar_altitude_meters ///////////////////\r\n", + "//\r\n", + "// Radar altitude (meters).\r\n", + "// See override_radar_location.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "radar_altitude_meters = -999;\r\n", + "\r\n", + "///////////// change_radar_latitude_sign //////////////\r\n", + "//\r\n", + "// Option to negate the latitude.\r\n", + "// Mainly useful for RAPIC files. In RAPIC, latitude is always positive, \r\n", + "// so mostly you need to set the latitiude to the negative value of \r\n", + "// itself.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "change_radar_latitude_sign = FALSE;\r\n", + "\r\n", + "///////////// apply_georeference_corrections //////////\r\n", + "//\r\n", + "// Option to apply the georeference info for moving platforms.\r\n", + "// For moving platforms, measured georeference information is sometimes \r\n", + "// available. If this is set to true, the georeference data is applied \r\n", + "// and appropriate corrections made. If possible, Earth-centric azimuth \r\n", + "// and elevation angles will be computed.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_georeference_corrections = FALSE;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO OVERRIDE SELECTED GLOBAL ATTRIBUTES.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// version_override ////////////////////////\r\n", + "//\r\n", + "// Option to override the version global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the version attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "version_override = \"\";\r\n", + "\r\n", + "///////////// title_override //////////////////////////\r\n", + "//\r\n", + "// Option to override the title global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the title attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "title_override = \"\";\r\n", + "\r\n", + "///////////// institution_override ////////////////////\r\n", + "//\r\n", + "// Option to override the institution global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the institution attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "institution_override = \"\";\r\n", + "\r\n", + "///////////// references_override /////////////////////\r\n", + "//\r\n", + "// Option to override the references global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the references attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "references_override = \"\";\r\n", + "\r\n", + "///////////// source_override /////////////////////////\r\n", + "//\r\n", + "// Option to override the source global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the source attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "source_override = \"\";\r\n", + "\r\n", + "///////////// history_override ////////////////////////\r\n", + "//\r\n", + "// Option to override the history global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the history attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "history_override = \"\";\r\n", + "\r\n", + "///////////// comment_override ////////////////////////\r\n", + "//\r\n", + "// Option to override the comment global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the comment attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "comment_override = \"\";\r\n", + "\r\n", + "///////////// author_override /////////////////////////\r\n", + "//\r\n", + "// Option to override the author global attribute.\r\n", + "// If empty, no effect. If not empty, this string is used to override \r\n", + "// the author attribute.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "author_override = \"\";\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO CORRECT ANTENNA ANGLES.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// apply_azimuth_offset ////////////////////\r\n", + "//\r\n", + "// Option to apply an offset to the azimuth values.\r\n", + "// If TRUE, this offset will be ADDED to the measured azimuth angles. \r\n", + "// This is useful, for example, in the case of a mobile platform which \r\n", + "// is not set up oriented to true north. Suppose you have a truck (like \r\n", + "// the DOWs) which is oriented off true north. Then if you add in the \r\n", + "// truck HEADING relative to true north, the measured azimuth angles \r\n", + "// will be adjusted by the heading, to give azimuth relative to TRUE \r\n", + "// north.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_azimuth_offset = FALSE;\r\n", + "\r\n", + "///////////// azimuth_offset //////////////////////////\r\n", + "//\r\n", + "// Azimuth offset (degrees).\r\n", + "// See 'apply_azimuth_offset'. This value will be ADDED to the measured \r\n", + "// azimuths.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "azimuth_offset = 0;\r\n", + "\r\n", + "///////////// apply_elevation_offset //////////////////\r\n", + "//\r\n", + "// Option to apply an offset to the elevation values.\r\n", + "// If TRUE, this offset will be ADDED to the measured elevation angles. \r\n", + "// This is useful to correct for a systematic bias in measured elevation \r\n", + "// angles.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_elevation_offset = FALSE;\r\n", + "\r\n", + "///////////// elevation_offset ////////////////////////\r\n", + "//\r\n", + "// Elevation offset (degrees).\r\n", + "// See 'apply_elevation_offset'. This value will be ADDED to the \r\n", + "// measured elevations.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "elevation_offset = 0;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO SPECIFY FIELD NAMES AND OUTPUT ENCODING.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// set_output_fields ///////////////////////\r\n", + "//\r\n", + "// Set the field names and output encoding.\r\n", + "// If false, all fields will be used.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_output_fields = TRUE;\r\n", + "\r\n", + "///////////// output_fields ///////////////////////////\r\n", + "//\r\n", + "// Output field details.\r\n", + "// Set the details for the output fields. The output_field_name is the \r\n", + "// ndtCDF variable name. Set the long name to a more descriptive name. \r\n", + "// Set the standard name to the CF standard name for this field. If the \r\n", + "// long name or standard name are empty, the existing names are used. If \r\n", + "// SCALING_SPECIFIED, then the scale and offset is used.\r\n", + "//\r\n", + "// Type: struct\r\n", + "// typedef struct {\r\n", + "// string input_field_name;\r\n", + "// string output_field_name;\r\n", + "// string long_name;\r\n", + "// string standard_name;\r\n", + "// string output_units;\r\n", + "// output_encoding_t encoding;\r\n", + "// Options:\r\n", + "// OUTPUT_ENCODING_ASIS\r\n", + "// OUTPUT_ENCODING_FLOAT32\r\n", + "// OUTPUT_ENCODING_INT32\r\n", + "// OUTPUT_ENCODING_INT16\r\n", + "// OUTPUT_ENCODING_INT08\r\n", + "// output_scaling_t output_scaling;\r\n", + "// Options:\r\n", + "// SCALING_DYNAMIC\r\n", + "// SCALING_SPECIFIED\r\n", + "// double output_scale;\r\n", + "// double output_offset;\r\n", + "// }\r\n", + "//\r\n", + "// 1D array - variable length.\r\n", + "//\r\n", + "\r\n", + "output_fields = {\r\n", + " {\r\n", + " input_field_name = \"REF\",\r\n", + " output_field_name = \"DBZ\",\r\n", + " long_name = \"radar_reflectivity\",\r\n", + " standard_name = \"equivalent_reflectivity_factor\",\r\n", + " output_units = \"dBZ\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"VEL\",\r\n", + " output_field_name = \"VEL\",\r\n", + " long_name = \"radial_velocity\",\r\n", + " standard_name = \"radial_velocity_of_scatterers_away_from_instrument\",\r\n", + " output_units = \"m/s\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"SW\",\r\n", + " output_field_name = \"WIDTH\",\r\n", + " long_name = \"spectrum_width\",\r\n", + " standard_name = \"doppler_spectrum_width\",\r\n", + " output_units = \"m/s\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"ZDR\",\r\n", + " output_field_name = \"ZDR\",\r\n", + " long_name = \"differential_reflectivity\",\r\n", + " standard_name = \"log_differential_reflectivity_hv\",\r\n", + " output_units = \"dB\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"PHI\",\r\n", + " output_field_name = \"PHIDP\",\r\n", + " long_name = \"differential_phase\",\r\n", + " standard_name = \"differential_phase_hv\",\r\n", + " output_units = \"deg\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"RHO\",\r\n", + " output_field_name = \"RHOHV\",\r\n", + " long_name = \"cross_correlation\",\r\n", + " standard_name = \"cross_correlation_ratio_hv\",\r\n", + " output_units = \"\",\r\n", + " encoding = OUTPUT_ENCODING_INT16,\r\n", + " output_scaling = SCALING_DYNAMIC,\r\n", + " output_scale = 0.01,\r\n", + " output_offset = 0\r\n", + " }\r\n", + "};\r\n", + "\r\n", + "///////////// write_other_fields_unchanged ////////////\r\n", + "//\r\n", + "// Option to write out the unspecified fields as they are.\r\n", + "// If false, only the fields listed in output_fields will be written. If \r\n", + "// this is true, all other fields will be written unchanged.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_other_fields_unchanged = FALSE;\r\n", + "\r\n", + "///////////// exclude_specified_fields ////////////////\r\n", + "//\r\n", + "// Option to exclude fields in the specified list.\r\n", + "// If true, the specified fields will be excluded. This may be easier \r\n", + "// than specifiying all of the fields to be included, if that list is \r\n", + "// very long.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "exclude_specified_fields = FALSE;\r\n", + "\r\n", + "///////////// excluded_fields /////////////////////////\r\n", + "//\r\n", + "// List of fields to be excluded.\r\n", + "// List the names to be excluded.\r\n", + "// Type: string\r\n", + "// 1D array - variable length.\r\n", + "//\r\n", + "\r\n", + "excluded_fields = {\r\n", + " \"DBZ\",\r\n", + " \"VEL\"\r\n", + "};\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO SPECIFY OUTPUT ENCODING FOR ALL FIELDS.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// set_output_encoding_for_all_fields //////\r\n", + "//\r\n", + "// Option to set output encoding for all fields.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "set_output_encoding_for_all_fields = FALSE;\r\n", + "\r\n", + "///////////// output_encoding /////////////////////////\r\n", + "//\r\n", + "// Output encoding for all fields, if requested.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// OUTPUT_ENCODING_ASIS\r\n", + "// OUTPUT_ENCODING_FLOAT32\r\n", + "// OUTPUT_ENCODING_INT32\r\n", + "// OUTPUT_ENCODING_INT16\r\n", + "// OUTPUT_ENCODING_INT08\r\n", + "//\r\n", + "\r\n", + "output_encoding = OUTPUT_ENCODING_ASIS;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// CENSORING.\r\n", + "//\r\n", + "// You have the option of censoring the data fields - i.e. setting the \r\n", + "// fields to missing values - at gates which meet certain criteria. If \r\n", + "// this is done correctly, it allows you to preserve the valid data and \r\n", + "// discard the noise, thereby improving compression.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// apply_censoring /////////////////////////\r\n", + "//\r\n", + "// Apply censoring based on field values and thresholds.\r\n", + "// If TRUE, censoring will be performed. See 'censoring_fields' for \r\n", + "// details on how the censoring is applied.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_censoring = FALSE;\r\n", + "\r\n", + "///////////// censoring_fields ////////////////////////\r\n", + "//\r\n", + "// Fields to be used for censoring.\r\n", + "// Specify the fields to be used to determine whether a gate should be \r\n", + "// censored. The name refers to the input data field names. Valid field \r\n", + "// values lie in the range from min_valid_value to max_valid_value \r\n", + "// inclusive. If the value of a field at a gate lies within this range, \r\n", + "// it is considered valid. Each specified field is examined at each \r\n", + "// gate, and is flagged as valid if its value lies in the valid range. \r\n", + "// These field flags are then combined as follows: first, all of the \r\n", + "// LOGICAL_OR flags are combined, yielding a single combined_or flag \r\n", + "// which is true if any of the LOGICAL_OR fields is true. The \r\n", + "// combined_or flag is then combined with all of the LOGICAL_AND fields, \r\n", + "// yielding a true value only if the combined_or flag and the \r\n", + "// LOGICAL_AND fields are all true. If this final flag is true, then the \r\n", + "// data at the gate is regarded as valid and is retained. If the final \r\n", + "// flag is false, the data at the gate is censored, and all of the \r\n", + "// fields at the gate are set to missing.\r\n", + "//\r\n", + "// Type: struct\r\n", + "// typedef struct {\r\n", + "// string name;\r\n", + "// double min_valid_value;\r\n", + "// double max_valid_value;\r\n", + "// logical_t combination_method;\r\n", + "// Options:\r\n", + "// LOGICAL_AND\r\n", + "// LOGICAL_OR\r\n", + "// }\r\n", + "//\r\n", + "// 1D array - variable length.\r\n", + "//\r\n", + "\r\n", + "censoring_fields = {\r\n", + " {\r\n", + " name = \"SNR\",\r\n", + " min_valid_value = 0,\r\n", + " max_valid_value = 1000,\r\n", + " combination_method = LOGICAL_OR\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " name = \"NCP\",\r\n", + " min_valid_value = 0.15,\r\n", + " max_valid_value = 1000,\r\n", + " combination_method = LOGICAL_OR\r\n", + " }\r\n", + "};\r\n", + "\r\n", + "///////////// censoring_min_valid_run /////////////////\r\n", + "//\r\n", + "// Minimum valid run of non-censored gates.\r\n", + "// Only active if set to 2 or greater. A check is made to remove short \r\n", + "// runs of noise. Looking along the radial, we compute the number of \r\n", + "// contiguous gates (a 'run') with uncensored data. For the gates in \r\n", + "// this run to be accepted the length of the run must exceed \r\n", + "// censoring_min_valid_run. If the number of gates in a run is less than \r\n", + "// this, then all gates in the run are censored.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "censoring_min_valid_run = 1;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO APPLY LINEAR TRANSFORM TO SPECIFIED FIELDS.\r\n", + "//\r\n", + "// These transforms are fixed. The same transform is applied to all \r\n", + "// files.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// apply_linear_transforms /////////////////\r\n", + "//\r\n", + "// Apply linear transform to specified fields.\r\n", + "// If true, we will apply a linear transform to selected fields.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_linear_transforms = FALSE;\r\n", + "\r\n", + "///////////// transform_fields ////////////////////////\r\n", + "//\r\n", + "// transform field details.\r\n", + "// Set the field name, scale and offset to be applied to the selected \r\n", + "// fields. NOTE: the field name is the INPUT field name.\r\n", + "//\r\n", + "// Type: struct\r\n", + "// typedef struct {\r\n", + "// string input_field_name;\r\n", + "// double transform_scale;\r\n", + "// double transform_offset;\r\n", + "// }\r\n", + "//\r\n", + "// 1D array - variable length.\r\n", + "//\r\n", + "\r\n", + "transform_fields = {\r\n", + " {\r\n", + " input_field_name = \"DBZ\",\r\n", + " transform_scale = 1,\r\n", + " transform_offset = 0\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"VEL\",\r\n", + " transform_scale = 1,\r\n", + " transform_offset = 0\r\n", + " }\r\n", + "};\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO APPLY VARIABLE LINEAR TRANSFORM TO SPECIFIED FIELDS.\r\n", + "//\r\n", + "// These transforms vary from file to file, controlled by specific \r\n", + "// metadata.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// apply_variable_transforms ///////////////\r\n", + "//\r\n", + "// Apply linear transforms that vary based on specific metadata.\r\n", + "// If true, we will apply variable linear transform to selected fields.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "apply_variable_transforms = FALSE;\r\n", + "\r\n", + "///////////// variable_transform_fields ///////////////\r\n", + "//\r\n", + "// Details for variable transforms.\r\n", + "// We based the field decision off the input_field_name. You need to \r\n", + "// pick the method of control: STATUS_XML_FIELD - based on the value \r\n", + "// associated with an XML tag in the status block; ELEVATION_DEG - based \r\n", + "// on the elevation in degrees; PULSE_WIDTH_US - based on the pulse with \r\n", + "// in microsecs. For STATUS_XML_FIELD Set the relevant status_xml_tag, \r\n", + "// which will be used to find the relevant value. The lookup table is a \r\n", + "// series of entries specifying the metadata_value and the scale and \r\n", + "// offset to be appied for that given metadata value. Each entry is \r\n", + "// enclosed in parentheses, and is of the form (metadata_value, scale, \r\n", + "// offset). The entries themselves are also are comma-separated. \r\n", + "// Interpolation is used for metadata values that lie between those \r\n", + "// specified in the lookup table. The enries in the lookup table should \r\n", + "// have metadata_values that are monotonically increasing.\r\n", + "//\r\n", + "// Type: struct\r\n", + "// typedef struct {\r\n", + "// string input_field_name;\r\n", + "// variable_transform_control_t control;\r\n", + "// Options:\r\n", + "// STATUS_XML_FIELD\r\n", + "// string xml_tag;\r\n", + "// string lookup_table;\r\n", + "// }\r\n", + "//\r\n", + "// 1D array - variable length.\r\n", + "//\r\n", + "\r\n", + "variable_transform_fields = {\r\n", + " {\r\n", + " input_field_name = \"dBZ\",\r\n", + " control = STATUS_XML_FIELD,\r\n", + " xml_tag = \"gdrxanctxfreq\",\r\n", + " lookup_table = \"(57.0, 1.0, -0.7), (60.0, 1.0, -0.2), (64.0, 1.0, -0.3), (67.0, 1.0, -1.8), (68.0, 1.0, -1.2), (69.0, 1.0, -1.3)\"\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"dBZv\",\r\n", + " control = STATUS_XML_FIELD,\r\n", + " xml_tag = \"gdrxanctxfreq\",\r\n", + " lookup_table = \"(57.0, 1.0, 0.1), (58.0, 1.0, 0.3), (60.0, 1.0, -0.3), (67.0, 1.0, -2.3), (69.0, 1.0, -2.0)\"\r\n", + " }\r\n", + " ,\r\n", + " {\r\n", + " input_field_name = \"ZDR\",\r\n", + " control = STATUS_XML_FIELD,\r\n", + " xml_tag = \"gdrxanctxfreq\",\r\n", + " lookup_table = \"(56.0, 1.0, -0.75), (58.0, 1.0, -0.75), (61.0, 1.0, 0.1), (63.5, 1.0, 0.2), (64.0, 1.0, 0.6), (69.0, 1.0, 0.6)\"\r\n", + " }\r\n", + "};\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OUTPUT FORMAT.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// output_format ///////////////////////////\r\n", + "//\r\n", + "// Format for the output files.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// OUTPUT_FORMAT_CFRADIAL\r\n", + "// OUTPUT_FORMAT_DORADE\r\n", + "// OUTPUT_FORMAT_FORAY\r\n", + "// OUTPUT_FORMAT_NEXRAD\r\n", + "// OUTPUT_FORMAT_UF\r\n", + "// OUTPUT_FORMAT_MDV_RADIAL\r\n", + "//\r\n", + "\r\n", + "output_format = OUTPUT_FORMAT_CFRADIAL;\r\n", + "\r\n", + "///////////// netcdf_style ////////////////////////////\r\n", + "//\r\n", + "// NetCDF style - if output_format is CFRADIAL.\r\n", + "// netCDF classic format, netCDF 64-bit offset format, netCDF4 using \r\n", + "// HDF5 format, netCDF4 using HDF5 format but only netCDF3 calls.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// CLASSIC\r\n", + "// NC64BIT\r\n", + "// NETCDF4\r\n", + "// NETCDF4_CLASSIC\r\n", + "//\r\n", + "\r\n", + "netcdf_style = NETCDF4;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OUTPUT BYTE-SWAPPING and COMPRESSION.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// output_native_byte_order ////////////////\r\n", + "//\r\n", + "// Option to leave data in native byte order.\r\n", + "// If false, data will be byte-swapped as appropriate on output.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "output_native_byte_order = FALSE;\r\n", + "\r\n", + "///////////// output_compressed ///////////////////////\r\n", + "//\r\n", + "// Option to compress data fields on output.\r\n", + "// Applies to netCDF and Dorade. UF does not support compression.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "output_compressed = TRUE;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OUTPUT OPTIONS FOR CfRadial FILES.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// output_force_ngates_vary ////////////////\r\n", + "//\r\n", + "// Option to force the use of ragged arrays for CfRadial files.\r\n", + "// Only applies to CfRadial. If true, forces the use of ragged arrays \r\n", + "// even if the number of gates for all rays is constant.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "output_force_ngates_vary = FALSE;\r\n", + "\r\n", + "///////////// compression_level ///////////////////////\r\n", + "//\r\n", + "// Compression level for output, if compressed.\r\n", + "// Applies to netCDF only. Dorade compression is run-length encoding, \r\n", + "// and has not options..\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "compression_level = 4;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OUTPUT DIRECTORY AND FILE NAME.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// output_dir //////////////////////////////\r\n", + "//\r\n", + "// Output directory path.\r\n", + "// Files will be written to this directory.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "output_dir = \"$(NEXRAD_DATA_DIR)/cfradial/moments/$(RADAR_NAME)\";\r\n", + "\r\n", + "///////////// output_filename_mode ////////////////////\r\n", + "//\r\n", + "// Mode for computing output file name.\r\n", + "// START_AND_END_TIMES: include both start and end times in file name. \r\n", + "// START_TIME_ONLY: include only start time in file name. END_TIME_ONLY: \r\n", + "// include only end time in file name. SPECIFY_FILE_NAME: file of this \r\n", + "// name will be written to output_dir.\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// START_AND_END_TIMES\r\n", + "// START_TIME_ONLY\r\n", + "// END_TIME_ONLY\r\n", + "// SPECIFY_FILE_NAME\r\n", + "//\r\n", + "\r\n", + "output_filename_mode = START_AND_END_TIMES;\r\n", + "\r\n", + "///////////// output_filename_prefix //////////////////\r\n", + "//\r\n", + "// Optional prefix for output filename.\r\n", + "// If empty, the standard prefix will be used.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "output_filename_prefix = \"\";\r\n", + "\r\n", + "///////////// include_instrument_name_in_file_name ////\r\n", + "//\r\n", + "// Option to include the instrument name in the file name.\r\n", + "// Default is true. Only applies to CfRadial files. If true, the \r\n", + "// instrument name will be included just before the volume number in the \r\n", + "// output file name.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "include_instrument_name_in_file_name = TRUE;\r\n", + "\r\n", + "///////////// include_subsecs_in_file_name ////////////\r\n", + "//\r\n", + "// Option to include sub-seconds in date-time part of file name.\r\n", + "// Default is true. Only applies to CfRadial files. If true, the \r\n", + "// millisecs of the start and end time will be included in the file \r\n", + "// name.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "include_subsecs_in_file_name = TRUE;\r\n", + "\r\n", + "///////////// use_hyphen_in_file_name_datetime_part ///\r\n", + "//\r\n", + "// Option to use a hyphen between date and time in filename.\r\n", + "// Default is false. Only applies to CfRadial files. Normally an \r\n", + "// underscore is used.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "use_hyphen_in_file_name_datetime_part = FALSE;\r\n", + "\r\n", + "///////////// output_filename /////////////////////////\r\n", + "//\r\n", + "// Name of output file.\r\n", + "// Applies only if output_filename_mode is SPECIFY_FILE_NAME. File of \r\n", + "// this name will be written to output_dir.\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "output_filename = \"cfradial.test.nc\";\r\n", + "\r\n", + "///////////// append_day_dir_to_output_dir ////////////\r\n", + "//\r\n", + "// Add the day directory to the output directory.\r\n", + "// Path will be output_dir/yyyymmdd/filename.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "append_day_dir_to_output_dir = TRUE;\r\n", + "\r\n", + "///////////// append_year_dir_to_output_dir ///////////\r\n", + "//\r\n", + "// Add the year directory to the output directory.\r\n", + "// Path will be output_dir/yyyy/yyyymmdd/filename.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "append_year_dir_to_output_dir = FALSE;\r\n", + "\r\n", + "///////////// write_individual_sweeps /////////////////\r\n", + "//\r\n", + "// Option to write out individual sweeps if appropriate.\r\n", + "// If true, the volume is split into individual sweeps for writing. \r\n", + "// Applies to CfRadial format. This is always true for DORADE format \r\n", + "// files.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_individual_sweeps = FALSE;\r\n", + "\r\n", + "///////////// write_latest_data_info //////////////////\r\n", + "//\r\n", + "// Option to write out _latest_data_info files.\r\n", + "// If true, the _latest_data_info files will be written after the \r\n", + "// converted file is written.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_latest_data_info = TRUE;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// OPTION TO OVERRIDE MISSING VALUES.\r\n", + "//\r\n", + "// Missing values are applicable to both metadata and field data. The \r\n", + "// default values should be satisfactory for most purposes. However, you \r\n", + "// can choose to override these if you are careful with the selected \r\n", + "// values.\r\n", + "\r\n", + "// The default values for metadata are:\r\n", + "// \tmissingMetaDouble = -9999.0\r\n", + "// \tmissingMetaFloat = -9999.0\r\n", + "// \tmissingMetaInt = -9999\r\n", + "// \tmissingMetaChar = -128\r\n", + "\r\n", + "// The default values for field data are:\r\n", + "// \tmissingFl64 = -9.0e33\r\n", + "// \tmissingFl32 = -9.0e33\r\n", + "// \tmissingSi32 = -2147483647\r\n", + "// \tmissingSi16 = -32768\r\n", + "// \tmissingSi08 = -128.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// override_missing_metadata_values ////////\r\n", + "//\r\n", + "// Option to override the missing values for meta-data.\r\n", + "// See following parameter options.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "override_missing_metadata_values = FALSE;\r\n", + "\r\n", + "///////////// missing_metadata_double /////////////////\r\n", + "//\r\n", + "// Missing value for metadata of type double.\r\n", + "// Only applies if override_missing_metadata_values is TRUE.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "missing_metadata_double = -9999;\r\n", + "\r\n", + "///////////// missing_metadata_float //////////////////\r\n", + "//\r\n", + "// Missing value for metadata of type float.\r\n", + "// Only applies if override_missing_metadata_values is TRUE.\r\n", + "// Type: float\r\n", + "//\r\n", + "\r\n", + "missing_metadata_float = -9999;\r\n", + "\r\n", + "///////////// missing_metadata_int ////////////////////\r\n", + "//\r\n", + "// Missing value for metadata of type int.\r\n", + "// Only applies if override_missing_metadata_values is TRUE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "missing_metadata_int = -9999;\r\n", + "\r\n", + "///////////// missing_metadata_char ///////////////////\r\n", + "//\r\n", + "// Missing value for metadata of type char.\r\n", + "// Only applies if override_missing_metadata_values is TRUE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "missing_metadata_char = -128;\r\n", + "\r\n", + "///////////// override_missing_field_values ///////////\r\n", + "//\r\n", + "// Option to override the missing values for field data.\r\n", + "// See following parameter options.\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "override_missing_field_values = FALSE;\r\n", + "\r\n", + "///////////// missing_field_fl64 //////////////////////\r\n", + "//\r\n", + "// Missing value for field data of type 64-bit float.\r\n", + "// Only applies if override_missing_field_values is TRUE.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "missing_field_fl64 = -9999;\r\n", + "\r\n", + "///////////// missing_field_fl32 //////////////////////\r\n", + "//\r\n", + "// Missing value for field data of type 32-bit float.\r\n", + "// Only applies if override_missing_field_values is TRUE.\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "missing_field_fl32 = -9999;\r\n", + "\r\n", + "///////////// missing_field_si32 //////////////////////\r\n", + "//\r\n", + "// Missing value for field data of type 32-bit integer.\r\n", + "// Only applies if override_missing_field_values is TRUE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "missing_field_si32 = -999999;\r\n", + "\r\n", + "///////////// missing_field_si16 //////////////////////\r\n", + "//\r\n", + "// Missing value for field data of type 16-bit integer.\r\n", + "// Only applies if override_missing_field_values is TRUE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "missing_field_si16 = -232768;\r\n", + "\r\n", + "///////////// missing_field_si08 //////////////////////\r\n", + "//\r\n", + "// Missing value for field data of type 8-bit integer.\r\n", + "// Only applies if override_missing_field_values is TRUE.\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "missing_field_si08 = -128;\r\n", + "\r\n" + ] + } + ], + "source": [ + "# View the param file\n", + "!cat ./params/RadxConvert.nexrad" + ] + }, + { + "cell_type": "markdown", + "id": "aabdc353", + "metadata": {}, + "source": [ + "## Convert raw nexrad files to CfRadial format" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5f9c2fd1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Program 'RadxConvert'\n", + "Run-time 2022/08/23 23:18:17.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220003_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220003_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296699.955749.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296699.955749.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220448_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220448_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296703.72851.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296703.72851.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220935_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220935_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296706.337293.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296706.337293.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221420_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221420_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296709.384453.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296709.384453.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221906_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221906_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296712.316541.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296712.316541.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222350_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222350_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296715.451022.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296715.451022.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222834_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222834_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296718.213276.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.20569.1661296718.213276.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + "======================================================================\n", + "Program 'RadxConvert'\n", + "Run-time 2022/08/23 23:18:39.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220249_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220249_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296721.479173.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296721.479173.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220723_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220723_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296724.703157.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296724.703157.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221204_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221204_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296727.891951.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296727.891951.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221633_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221633_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296730.935994.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296730.935994.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222102_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222102_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296734.109793.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296734.109793.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222531_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222531_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296737.35478.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.20588.1661296737.35478.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + "======================================================================\n", + "Program 'RadxConvert'\n", + "Run-time 2022/08/23 23:18:58.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220000_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220000_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296740.562900.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296740.562900.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220430_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220430_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296744.43960.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296744.43960.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220921_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220921_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296747.410519.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296747.410519.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_221600_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_221600_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296750.664431.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296750.664431.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222051_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222051_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296754.68540.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296754.68540.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + "INFO - RadxConvert::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222533_V06\n", + "WARNING - NexradRadxFile::readFromPath\n", + " Adaptation data probably not set, ignoring\n", + " File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222533_V06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296757.488861.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: DBZ\n", + " ... writing field: VEL\n", + " ... writing field: WIDTH\n", + " ... writing field: ZDR\n", + " ... writing field: PHIDP\n", + " ... writing field: RHOHV\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.20608.1661296757.488861.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n" + ] + } + ], + "source": [ + "# Convert raw nexrad data to cfradial for 3 NEXRAD radars\n", + "\n", + "for radar_name in ['KGLD', 'KUEX', 'KDDC']:\n", + " # Set radar in name environment variable\n", + " os.environ['RADAR_NAME'] = radar_name\n", + " # Run RadxConvert using param file\n", + " !/usr/local/lrose/bin/RadxConvert -params ./params/RadxConvert.nexrad -debug -start \"2021 07 06 22 00 00\" -end \"2021 07 06 22 30 00\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac26ceb6", + "metadata": {}, + "source": [ + "## List the CfRadial files created by RadxConvert" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "100476d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706:\r\n", + "cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\r\n", + "cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\r\n", + "cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\r\n", + "cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\r\n", + "cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\r\n", + "cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706:\r\n", + "cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\r\n", + "cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\r\n", + "cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\r\n", + "cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\r\n", + "cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\r\n", + "cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\r\n", + "cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706:\r\n", + "cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\r\n", + "cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\r\n", + "cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\r\n", + "cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\r\n", + "cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\r\n", + "cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\r\n" + ] + } + ], + "source": [ + "# List the CfRadial files created by RadxConvert\n", + "!ls -R ${NEXRAD_DATA_DIR}/cfradial/moments/K*/20*" + ] + }, + { + "cell_type": "markdown", + "id": "e885ed48", + "metadata": {}, + "source": [ + "## Plot one example of the NEXRAD Goodland radar (KGLD) CfRadial files" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "304e140e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "altitude: \n", + "altitude_agl: \n", + "antenna_transition: \n", + "azimuth: \n", + "elevation: \n", + "fields:\n", + "\tDBZ: \n", + "\tVEL: \n", + "\tWIDTH: \n", + "\tZDR: \n", + "\tPHIDP: \n", + "\tRHOHV: \n", + "fixed_angle: \n", + "instrument_parameters:\n", + "\tfollow_mode: \n", + "\tpulse_width: \n", + "\tprt_mode: \n", + "\tprt: \n", + "\tprt_ratio: \n", + "\tpolarization_mode: \n", + "\tnyquist_velocity: \n", + "\tunambiguous_range: \n", + "\tn_samples: \n", + "\tradar_antenna_gain_h: \n", + "\tradar_antenna_gain_v: \n", + "\tradar_beam_width_h: \n", + "\tradar_beam_width_v: \n", + "\tradar_rx_bandwidth: \n", + "\tmeasured_transmit_power_v: \n", + "\tmeasured_transmit_power_h: \n", + "latitude: \n", + "longitude: \n", + "nsweeps: 14\n", + "ngates: 912\n", + "nrays: 6120\n", + "radar_calibration:\n", + "\tr_calib_time: \n", + "\tr_calib_pulse_width: \n", + "\tr_calib_xmit_power_h: \n", + "\tr_calib_xmit_power_v: \n", + "\tr_calib_two_way_waveguide_loss_h: \n", + "\tr_calib_two_way_waveguide_loss_v: \n", + "\tr_calib_two_way_radome_loss_h: \n", + "\tr_calib_two_way_radome_loss_v: \n", + "\tr_calib_receiver_mismatch_loss: \n", + "\tr_calib_k_squared_water: \n", + "\tr_calib_radar_constant_h: \n", + "\tr_calib_radar_constant_v: \n", + "\tr_calib_antenna_gain_h: \n", + "\tr_calib_antenna_gain_v: \n", + "\tr_calib_noise_hc: \n", + "\tr_calib_noise_vc: \n", + "\tr_calib_noise_hx: \n", + "\tr_calib_noise_vx: \n", + "\tr_calib_i0_dbm_hc: \n", + "\tr_calib_i0_dbm_vc: \n", + "\tr_calib_i0_dbm_hx: \n", + "\tr_calib_i0_dbm_vx: \n", + "\tr_calib_receiver_gain_hc: \n", + "\tr_calib_receiver_gain_vc: \n", + "\tr_calib_receiver_gain_hx: \n", + "\tr_calib_receiver_gain_vx: \n", + "\tr_calib_receiver_slope_hc: \n", + "\tr_calib_receiver_slope_vc: \n", + "\tr_calib_receiver_slope_hx: \n", + "\tr_calib_receiver_slope_vx: \n", + "\tr_calib_dynamic_range_db_hc: \n", + "\tr_calib_dynamic_range_db_vc: \n", + "\tr_calib_dynamic_range_db_hx: \n", + "\tr_calib_dynamic_range_db_vx: \n", + "\tr_calib_base_dbz_1km_hc: \n", + "\tr_calib_base_dbz_1km_vc: \n", + "\tr_calib_base_dbz_1km_hx: \n", + "\tr_calib_base_dbz_1km_vx: \n", + "\tr_calib_sun_power_hc: \n", + "\tr_calib_sun_power_vc: \n", + "\tr_calib_sun_power_hx: \n", + "\tr_calib_sun_power_vx: \n", + "\tr_calib_noise_source_power_h: \n", + "\tr_calib_noise_source_power_v: \n", + "\tr_calib_power_measure_loss_h: \n", + "\tr_calib_power_measure_loss_v: \n", + "\tr_calib_coupler_forward_loss_h: \n", + "\tr_calib_coupler_forward_loss_v: \n", + "\tr_calib_dbz_correction: \n", + "\tr_calib_zdr_correction: \n", + "\tr_calib_ldr_correction_h: \n", + "\tr_calib_ldr_correction_v: \n", + "\tr_calib_system_phidp: \n", + "\tr_calib_test_power_h: \n", + "\tr_calib_test_power_v: \n", + "\tr_calib_index: \n", + "range: \n", + "scan_rate: \n", + "scan_type: ppi\n", + "sweep_end_ray_index: \n", + "sweep_mode: \n", + "sweep_number: \n", + "sweep_start_ray_index: \n", + "target_scan_rate: \n", + "time: \n", + "metadata:\n", + "\tConventions: CF-1.7\n", + "\tSub_conventions: CF-Radial instrument_parameters radar_parameters radar_calibration\n", + "\tversion: CF-Radial-1.4\n", + "\ttitle: \n", + "\tinstitution: \n", + "\treferences: \n", + "\tsource: ARCHIVE 2 data\n", + "\thistory: \n", + "\tcomment: \n", + "\toriginal_format: NEXRAD\n", + "\tdriver: RadxConvert(NCAR)\n", + "\tcreated: 2022/08/23 23:18:19.955\n", + "\tstart_datetime: 2021-07-06T22:00:03Z\n", + "\ttime_coverage_start: 2021-07-06T22:00:03Z\n", + "\tstart_time: 2021-07-06 22:00:03.963\n", + "\tend_datetime: 2021-07-06T22:04:39Z\n", + "\ttime_coverage_end: 2021-07-06T22:04:39Z\n", + "\tend_time: 2021-07-06 22:04:39.770\n", + "\tinstrument_name: KGLD\n", + "\tsite_name: DLGK\n", + "\tscan_name: Surveillance\n", + "\tscan_id: 212\n", + "\tplatform_is_mobile: false\n", + "\tn_gates_vary: false\n", + "\tray_times_increase: true\n", + "\tvolume_number: 79\n", + "\tplatform_type: fixed\n", + "\tinstrument_type: radar\n", + "\tprimary_axis: axis_z\n" + ] + } + ], + "source": [ + "# Read CfRadial file into radar object\n", + "filePathRadar = os.path.join(nexradDataDir, \"cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\")\n", + "radar_kgld = pyart.io.read_cfradial(filePathRadar)\n", + "radar_kgld.info('compact')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3e0f3e2c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot KGLD fields from this CfRadial file\n", + "\n", + "displayKgld = pyart.graph.RadarDisplay(radar_kgld)\n", + "figKgld = plt.figure(1, (12, 10))\n", + "\n", + "# DBZ\n", + "\n", + "axDbz = figKgld.add_subplot(221)\n", + "displayKgld.plot_ppi('DBZ', 0, vmin=-32, vmax=64.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"DBZ\")\n", + "displayKgld.plot_range_rings([50, 100, 150, 200])\n", + "displayKgld.plot_cross_hair(200.)\n", + "\n", + "# VEL\n", + "\n", + "axVel = figKgld.add_subplot(222)\n", + "displayKgld.plot_ppi('VEL', 0, vmin=-30, vmax=30.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"VEL(m/s)\",\n", + " cmap = \"rainbow\")\n", + "displayKgld.plot_range_rings([50, 100, 150, 200])\n", + "displayKgld.plot_cross_hair(200.)\n", + "\n", + "# ZDR\n", + "\n", + "axZdr = figKgld.add_subplot(223)\n", + "displayKgld.plot_ppi('ZDR', 0, vmin=-4, vmax=16.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"ZDR(dB)\",\n", + " cmap = \"rainbow\")\n", + "displayKgld.plot_range_rings([50, 100, 150, 200])\n", + "displayKgld.plot_cross_hair(200.)\n", + "\n", + "# PHIDP\n", + "\n", + "axPhidp = figKgld.add_subplot(224)\n", + "displayKgld.plot_ppi('PHIDP', 0,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"PHIDP(deg)\",\n", + " cmap = \"rainbow\")\n", + "displayKgld.plot_range_rings([50, 100, 150, 200])\n", + "displayKgld.plot_cross_hair(200.)\n", + "\n", + "# set layout and display\n", + "\n", + "figKgld.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3938166f", + "metadata": {}, + "source": [ + "### Read in temperature field from RUC model, to provide temperature profile for PID and Ecco" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "df242020", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "root group (NETCDF4 data model, file format HDF5):\n", + " Conventions: CF-1.6\n", + " history: Converted from NetCDF to MDV, 2022/08/21 03:33:17\n", + " Ncf:comment: \n", + "\n", + " source: Grib2\n", + " title: RUC Rapid Refresh\n", + " comment: \n", + " dimensions(sizes): time(1), bounds(2), x0(139), y0(72), z0(32), nbytes_mdv_chunk_0000(174)\n", + " variables(dimensions): float64 time(time), float64 forecast_reference_time(time), float64 forecast_period(time), float64 start_time(time), float64 stop_time(time), float32 x0(x0), float32 y0(y0), float32 z0(z0), int32 grid_mapping_0(), int32 mdv_master_header(time), int8 mdv_chunk_0000(time, nbytes_mdv_chunk_0000), float32 TMP(time, z0, y0, x0), float32 RH(time, z0, y0, x0), float32 UGRD(time, z0, y0, x0), float32 VGRD(time, z0, y0, x0), float32 VVEL(time, z0, y0, x0), float32 HGT(time, z0, y0, x0), float32 Pressure(time, z0, y0, x0)\n", + " groups: \n", + "Start time model: 2021/07/06-17:00:00 UTC\n", + "nZModel, nYModel, nXModel 32 72 139\n", + "minLonModel, maxLonModel: -110.42499923706055 -89.57499313354492\n", + "minLatModel, maxLatModel: 34.625 45.42500305175781\n", + "minHt, maxHt: 1.2202008 16.17987\n", + "Model hts: [ 1.2202008 1.4573147 1.7001446 1.9490081 2.2042506 2.4662497\n", + " 2.7354188 3.0122116 3.2971282 3.5907218 3.893606 4.2064652\n", + " 4.530064 4.865264 5.213037 5.574489 5.950885 6.343679\n", + " 6.75456 7.185502 7.6388354 8.117341 8.624373 9.164038\n", + " 9.741439 10.363035 11.037209 11.784151 12.630965 13.60854\n", + " 14.764765 16.17987 ]\n" + ] + } + ], + "source": [ + "filePathModel = os.path.join(nexradDataDir, 'mdv/ruc/20210706/20210706_230000.mdv.cf.nc')\n", + "dsModel = nc.Dataset(filePathModel)\n", + "print(dsModel)\n", + "dstemp = dsModel['TMP']\n", + "temp3D = np.array(dstemp)\n", + "fillValueTemp = dstemp._FillValue\n", + "if (len(temp3D.shape) == 4):\n", + " temp3D = temp3D[0]\n", + "\n", + "# Compute time\n", + "\n", + "uTimeSecsModel = dsModel['start_time'][0]\n", + "startTimeModel = datetime.datetime.fromtimestamp(int(uTimeSecsModel))\n", + "startTimeStrModel = startTimeModel.strftime('%Y/%m/%d-%H:%M:%S UTC')\n", + "print(\"Start time model: \", startTimeStrModel)\n", + "\n", + "# Compute Model grid limits\n", + "(nZModel, nYModel, nXModel) = temp3D.shape\n", + "lon = np.array(dsModel['x0'])\n", + "lat = np.array(dsModel['y0'])\n", + "ht = np.array(dsModel['z0'])\n", + "dLonModel = lon[1] - lon[0]\n", + "dLatModel = lat[1] - lat[0]\n", + "minLonModel = lon[0] - dLonModel / 2.0\n", + "maxLonModel = lon[-1] + dLonModel / 2.0\n", + "minLatModel = lat[0] - dLatModel / 2.0\n", + "maxLatModel = lat[-1] + dLatModel / 2.0\n", + "minHtModel = ht[0]\n", + "maxHtModel = ht[-1]\n", + "print(\"nZModel, nYModel, nXModel\", nZModel, nYModel, nXModel)\n", + "print(\"minLonModel, maxLonModel: \", minLonModel, maxLonModel)\n", + "print(\"minLatModel, maxLatModel: \", minLatModel, maxLatModel)\n", + "print(\"minHt, maxHt: \", minHtModel, maxHtModel)\n", + "print(\"Model hts: \", ht)\n", + "del lon, lat, ht" + ] + }, + { + "cell_type": "markdown", + "id": "22251ec0", + "metadata": {}, + "source": [ + "## Plot W-E and N-S vertical sections of temperature data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "68f66246", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(32, 72)\n", + "(32, 1, 139)\n", + "(32, 139)\n" + ] + } + ], + "source": [ + "# Compute Temp N-S vertical section\n", + "nXHalfModel = int(nXModel/2)\n", + "tempVertNS = temp3D[:, :, nXHalfModel:(nXHalfModel+1)]\n", + "tempVertNS = tempVertNS.reshape(tempVertNS.shape[0], tempVertNS.shape[1])\n", + "tempVertNS[tempVertNS == fillValueTemp] = np.nan\n", + "print(tempVertNS.shape)\n", + "tempNSMax = np.amax(temp3D, (2))\n", + "tempNSMax[tempNSMax == fillValueTemp] = np.nan\n", + "\n", + "# Compute Temp W-E vertical section\n", + "nYHalfModel = int(nYModel/2)\n", + "tempVertWE = temp3D[:, nYHalfModel:(nYHalfModel+1), :]\n", + "print(tempVertWE.shape)\n", + "tempVertWE = tempVertWE.reshape(tempVertWE.shape[0], tempVertWE.shape[2])\n", + "tempVertWE[tempVertWE == fillValueTemp] = np.nan\n", + "tempWEMax = np.amax(temp3D, (1))\n", + "tempWEMax[tempWEMax == fillValueTemp] = np.nan\n", + "print(tempVertWE.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6f246ac8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Vert slice mid N-S temp: 2021/07/06-17:00:00 UTC')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot model temp vertical sections at mid-points of grid\n", + "\n", + "figModelTemp = plt.figure(num=1, figsize=[12, 8], layout='constrained')\n", + "\n", + "# Plot W-E temp\n", + "\n", + "axWETemp = figModelTemp.add_subplot(1, 2, 1,\n", + " xlim = (minLonModel, maxLonModel),\n", + " ylim = (minHtModel, maxHtModel))\n", + "plt.imshow(tempVertWE,\n", + " cmap='jet',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLonModel, maxLonModel, minHtModel, maxHtModel))\n", + "axWETemp.set_aspect(1.0)\n", + "axWETemp.set_xlabel('Longitude (deg)')\n", + "axWETemp.set_ylabel('Height (km)')\n", + "plt.colorbar(label=\"Temperature (C)\", orientation=\"horizontal\", fraction=0.1)\n", + "plt.title(\"Vert slice mid W-E temp: \" + startTimeStrModel)\n", + "\n", + "# Plot N-S temp vertical section\n", + "\n", + "axNSTemp = figModelTemp.add_subplot(1, 2, 2, \n", + " xlim = (minLatModel, maxLatModel),\n", + " ylim = (minHtModel, maxHtModel))\n", + "plt.imshow(tempVertNS,\n", + " cmap='jet',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLatModel, maxLatModel, minHtModel, maxHtModel))\n", + "axNSTemp.set_aspect(0.52)\n", + "axNSTemp.set_xlabel('Latitude (deg)')\n", + "axNSTemp.set_ylabel('Height (km)')\n", + "plt.colorbar(label=\"Temperature (C)\", orientation=\"horizontal\", fraction=0.1)\n", + "plt.title(\"Vert slice mid N-S temp: \" + startTimeStrModel)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f82ae872", + "metadata": {}, + "source": [ + "## Sample RUC temperatures at 3 radar locations, save in SPDB data base" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3d52525d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Program 'Mdv2SoundingSpdb'\n", + "Run-time 2022/08/23 23:19:48.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Processing file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - setting master header\n", + "Default time dimension: time\n", + " time: 2021/07/06 23:00:00\n", + "SUCCESS - setting time coord variable\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: TMP\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: TMP\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: TMP\n", + "Adding data field: TMP\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: RH\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: RH\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: RH\n", + "Adding data field: RH\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: UGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: UGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: UGRD\n", + "Adding data field: UGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VGRD\n", + "Adding data field: VGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VVEL\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VVEL\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VVEL\n", + "Adding data field: VVEL\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: HGT\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: HGT\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: HGT\n", + "Adding data field: HGT\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: Pressure\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: Pressure\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: Pressure\n", + "Adding data field: Pressure\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::addDataFieldsTime elapsed = 0\n", + "Adding chunk: NetCDF file global attributes\n", + "Ncf2MdvTrans::addGlobalAttrXmlChunk()\n", + "Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc\n", + " Station name : KGLD\n", + " Sounding time: 2021/07/06 23:00:00\n", + "Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - setting master header\n", + "Default time dimension: time\n", + " time: 2021/07/06 23:00:00\n", + "SUCCESS - setting time coord variable\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: TMP\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: TMP\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: TMP\n", + "Adding data field: TMP\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: RH\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: RH\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: RH\n", + "Adding data field: RH\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: UGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: UGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: UGRD\n", + "Adding data field: UGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VGRD\n", + "Adding data field: VGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VVEL\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VVEL\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VVEL\n", + "Adding data field: VVEL\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: HGT\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: HGT\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: HGT\n", + "Adding data field: HGT\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: Pressure\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: Pressure\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: Pressure\n", + "Adding data field: Pressure\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::addDataFieldsTime elapsed = 0\n", + "Adding chunk: NetCDF file global attributes\n", + "Ncf2MdvTrans::addGlobalAttrXmlChunk()\n", + "Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc\n", + " Station name : KDDC\n", + " Sounding time: 2021/07/06 23:00:00\n", + "Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc\n", + "SUCCESS - setting master header\n", + "Default time dimension: time\n", + " time: 2021/07/06 23:00:00\n", + "SUCCESS - setting time coord variable\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> rejecting field: mdv_chunk_0000\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: TMP\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: TMP\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: TMP\n", + "Adding data field: TMP\n", + " time: 2021/07/06 23:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: RH\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: RH\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: RH\n", + "Adding data field: RH\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: UGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: UGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: UGRD\n", + "Adding data field: UGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VGRD\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VGRD\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VGRD\n", + "Adding data field: VGRD\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: VVEL\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: VVEL\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: VVEL\n", + "Adding data field: VVEL\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: HGT\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: HGT\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: HGT\n", + "Adding data field: HGT\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::_shouldAddField\n", + " -->> adding field: Pressure\n", + "Ncf2MdvTrans::_shouldAddField\n", + " Checking variable for field data: Pressure\n", + "SUCCESS - var has X coordinate, dim: x0\n", + "SUCCESS - var has Y coordinate, dim: y0\n", + "NOTE - var has Z coordinate, dim: z0\n", + "Ncf2MdvTrans::_addOneField\n", + " -->> adding field: Pressure\n", + "Adding data field: Pressure\n", + " time: 2021/07/06 23:00:00\n", + "Ncf2MdvTrans::addDataFieldsTime elapsed = 0\n", + "Adding chunk: NetCDF file global attributes\n", + "Ncf2MdvTrans::addGlobalAttrXmlChunk()\n", + "Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc\n", + " Station name : KUEX\n", + " Sounding time: 2021/07/06 23:00:00\n" + ] + } + ], + "source": [ + "# Run Mdv2SoundingSpdb to sample temperature data and store in SPDB\n", + "!/usr/local/lrose/bin/Mdv2SoundingSpdb -debug -params params/Mdv2SoundingSpdb.ruc -start \"2021 07 06 00 00 00\" -end \"2021 07 07 00 00 00\"" + ] + }, + { + "cell_type": "markdown", + "id": "b69e40db", + "metadata": {}, + "source": [ + "## List sounding data base" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7b67ce2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 mdtest mdtest 4704 Aug 23 17:19 /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706.data\r\n", + "-rw-rw-r-- 1 mdtest mdtest 6360 Aug 23 17:19 /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706.indx\r\n" + ] + } + ], + "source": [ + "# List SPDB files\n", + "!ls -alR /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706*" + ] + }, + { + "cell_type": "markdown", + "id": "e94d4288", + "metadata": {}, + "source": [ + "## Run RadxRate on the CfRadial files\n", + "\n", + "RadxRate will compute:\n", + "\n", + "* KDP - specific differential phase\n", + "* KDP_SC - KDP conditioned using Z and ZDR self-consistency\n", + "* PID - NCAR Particle ID (type)\n", + "* RATE_ZH - precip rate from standard ZR relationship\n", + "* RATE_HYBRID - precip rate from NCAR hybrid estimator\n", + "\n", + "RadxRate has a main parameter file, which then specifies a parameter file computing each of KDP, PID and precip rate.\n", + "\n", + "The PID step requires an additional parameter file specifying the fuzzy logic thresholds.\n", + "\n", + "The parameter files used here are:\n", + "\n", + "* kdp_params.nexrad\n", + "* pid_params.nexrad\n", + "* pid_thresholds.nexrad\n", + "* rate_params.nexrad" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ae0bae96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RadxRate::_runArchive\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:00:03\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296815.701177.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296815.701177.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:04:48\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296819.95268.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296819.95268.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:09:35\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296822.500949.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296822.500949.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:14:20\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296825.915613.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296825.915613.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:19:06\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296829.351126.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296829.351126.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:23:50\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296832.607201.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296832.607201.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:28:34\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296835.977260.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.20812.1661296835.977260.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + "RadxRate::_runArchive\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:02:49\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296840.563117.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296840.563117.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:07:23\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296843.953921.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296843.953921.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:12:04\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296847.438182.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296847.438182.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:16:33\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296850.862523.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296850.862523.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:21:02\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296854.79423.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296854.79423.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:25:31\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296857.317240.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.20839.1661296857.317240.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + "RadxRate::_runArchive\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:00:00\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296861.822721.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296861.822721.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:04:30\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296865.86686.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296865.86686.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:09:21\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296868.502222.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296868.502222.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:16:00\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296871.788824.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296871.788824.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:20:51\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296875.71437.tmp\n", + " Writing fields and compressing ...\n", + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296875.71437.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + "INFO - RadxRate::Run\n", + " Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + "Thread #: 0\n", + " Loading temp profile for time: 2021/07/06 22:25:33\n", + "INFO: RadxFile::writeToDir\n", + " Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToDir\n", + " Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + " Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296878.543055.tmp\n", + " Writing fields and compressing ...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ... writing field: RATE_ZH\n", + " ... writing field: RATE_HYBRID\n", + " ... writing field: PID\n", + " ... writing field: KDP\n", + " ... writing field: DBZ\n", + "DEBUG - NcfRadxFile::writeToPath\n", + " Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.20871.1661296878.543055.tmp\n", + " to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + "INFO: RadxFile::writeToDir\n", + " Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n" + ] + } + ], + "source": [ + "# Run RadxRate for 3 NEXRAD radars\n", + "\n", + "for radar_name in ['KGLD', 'KUEX', 'KDDC']:\n", + " # Set radar in name environment variable\n", + " os.environ['RADAR_NAME'] = radar_name\n", + " # Run RadxRate using param file\n", + " !/usr/local/lrose/bin/RadxRate -params ./params/RadxRate.nexrad -debug -start \"2021 07 06 22 00 00\" -end \"2021 07 06 22 30 00\"" + ] + }, + { + "cell_type": "markdown", + "id": "d0bf3b05", + "metadata": {}, + "source": [ + "## List files created by RadxRate" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8c4c3d58", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706:\r\n", + "cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\r\n", + "cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\r\n", + "cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\r\n", + "cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\r\n", + "cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\r\n", + "cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706:\r\n", + "cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\r\n", + "cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\r\n", + "cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\r\n", + "cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\r\n", + "cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\r\n", + "cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\r\n", + "cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706:\r\n", + "cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\r\n", + "cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\r\n", + "cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\r\n", + "cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\r\n", + "cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\r\n", + "cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\r\n" + ] + } + ], + "source": [ + "# List the CfRadial files created by RadxRate\n", + "!ls -R ${NEXRAD_DATA_DIR}/cfradial/rate/K*/2*" + ] + }, + { + "cell_type": "markdown", + "id": "36620eb0", + "metadata": {}, + "source": [ + "## Plot PID and rate results for NEXRAD Goodland radar (KGLD)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "470ba92b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "altitude: \n", + "altitude_agl: \n", + "antenna_transition: \n", + "azimuth: \n", + "elevation: \n", + "fields:\n", + "\tRATE_ZH: \n", + "\tRATE_HYBRID: \n", + "\tPID: \n", + "\tKDP: \n", + "\tDBZ: \n", + "fixed_angle: \n", + "instrument_parameters:\n", + "\tfollow_mode: \n", + "\tpulse_width: \n", + "\tprt_mode: \n", + "\tprt: \n", + "\tprt_ratio: \n", + "\tpolarization_mode: \n", + "\tnyquist_velocity: \n", + "\tunambiguous_range: \n", + "\tn_samples: \n", + "\tradar_antenna_gain_h: \n", + "\tradar_antenna_gain_v: \n", + "\tradar_beam_width_h: \n", + "\tradar_beam_width_v: \n", + "\tradar_rx_bandwidth: \n", + "\tmeasured_transmit_power_v: \n", + "\tmeasured_transmit_power_h: \n", + "latitude: \n", + "longitude: \n", + "nsweeps: 14\n", + "ngates: 912\n", + "nrays: 6120\n", + "radar_calibration:\n", + "\tr_calib_time: \n", + "\tr_calib_pulse_width: \n", + "\tr_calib_xmit_power_h: \n", + "\tr_calib_xmit_power_v: \n", + "\tr_calib_two_way_waveguide_loss_h: \n", + "\tr_calib_two_way_waveguide_loss_v: \n", + "\tr_calib_two_way_radome_loss_h: \n", + "\tr_calib_two_way_radome_loss_v: \n", + "\tr_calib_receiver_mismatch_loss: \n", + "\tr_calib_k_squared_water: \n", + "\tr_calib_radar_constant_h: \n", + "\tr_calib_radar_constant_v: \n", + "\tr_calib_antenna_gain_h: \n", + "\tr_calib_antenna_gain_v: \n", + "\tr_calib_noise_hc: \n", + "\tr_calib_noise_vc: \n", + "\tr_calib_noise_hx: \n", + "\tr_calib_noise_vx: \n", + "\tr_calib_i0_dbm_hc: \n", + "\tr_calib_i0_dbm_vc: \n", + "\tr_calib_i0_dbm_hx: \n", + "\tr_calib_i0_dbm_vx: \n", + "\tr_calib_receiver_gain_hc: \n", + "\tr_calib_receiver_gain_vc: \n", + "\tr_calib_receiver_gain_hx: \n", + "\tr_calib_receiver_gain_vx: \n", + "\tr_calib_receiver_slope_hc: \n", + "\tr_calib_receiver_slope_vc: \n", + "\tr_calib_receiver_slope_hx: \n", + "\tr_calib_receiver_slope_vx: \n", + "\tr_calib_dynamic_range_db_hc: \n", + "\tr_calib_dynamic_range_db_vc: \n", + "\tr_calib_dynamic_range_db_hx: \n", + "\tr_calib_dynamic_range_db_vx: \n", + "\tr_calib_base_dbz_1km_hc: \n", + "\tr_calib_base_dbz_1km_vc: \n", + "\tr_calib_base_dbz_1km_hx: \n", + "\tr_calib_base_dbz_1km_vx: \n", + "\tr_calib_sun_power_hc: \n", + "\tr_calib_sun_power_vc: \n", + "\tr_calib_sun_power_hx: \n", + "\tr_calib_sun_power_vx: \n", + "\tr_calib_noise_source_power_h: \n", + "\tr_calib_noise_source_power_v: \n", + "\tr_calib_power_measure_loss_h: \n", + "\tr_calib_power_measure_loss_v: \n", + "\tr_calib_coupler_forward_loss_h: \n", + "\tr_calib_coupler_forward_loss_v: \n", + "\tr_calib_dbz_correction: \n", + "\tr_calib_zdr_correction: \n", + "\tr_calib_ldr_correction_h: \n", + "\tr_calib_ldr_correction_v: \n", + "\tr_calib_system_phidp: \n", + "\tr_calib_test_power_h: \n", + "\tr_calib_test_power_v: \n", + "\tr_calib_index: \n", + "range: \n", + "scan_rate: \n", + "scan_type: ppi\n", + "sweep_end_ray_index: \n", + "sweep_mode: \n", + "sweep_number: \n", + "sweep_start_ray_index: \n", + "target_scan_rate: \n", + "time: \n", + "metadata:\n", + "\tConventions: CF-1.7\n", + "\tSub_conventions: CF-Radial instrument_parameters radar_parameters radar_calibration\n", + "\tversion: CF-Radial-1.4\n", + "\ttitle: \n", + "\tinstitution: \n", + "\treferences: \n", + "\tsource: ARCHIVE 2 data\n", + "\thistory: \n", + "\tcomment: \n", + "\toriginal_format: NEXRAD\n", + "\tdriver: RadxConvert(NCAR)\n", + "\tcreated: 2022/08/23 23:18:19.955\n", + "\tstart_datetime: 2021-07-06T22:00:03Z\n", + "\ttime_coverage_start: 2021-07-06T22:00:03Z\n", + "\tstart_time: 2021-07-06 22:00:03.963\n", + "\tend_datetime: 2021-07-06T22:04:39Z\n", + "\ttime_coverage_end: 2021-07-06T22:04:39Z\n", + "\tend_time: 2021-07-06 22:04:39.770\n", + "\tinstrument_name: KGLD\n", + "\tsite_name: DLGK\n", + "\tscan_name: Surveillance\n", + "\tscan_id: 212\n", + "\tplatform_is_mobile: false\n", + "\tn_gates_vary: false\n", + "\tray_times_increase: true\n", + "\tvolume_number: 79\n", + "\tplatform_type: fixed\n", + "\tinstrument_type: radar\n", + "\tprimary_axis: axis_z\n" + ] + } + ], + "source": [ + "# Read CfRadial file into radar object\n", + "filePathRate = os.path.join(nexradDataDir, \"cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\")\n", + "rate_kgld = pyart.io.read_cfradial(filePathRate)\n", + "rate_kgld.info('compact')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "df337ade", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results of RadxRate\n", + "\n", + "displayRate = pyart.graph.RadarDisplay(rate_kgld)\n", + "figRate = plt.figure(1, (12, 10))\n", + "\n", + "# DBZ (input)\n", + "\n", + "axDbz = figRate.add_subplot(221)\n", + "displayRate.plot_ppi('DBZ', 0, vmin=-32, vmax=64.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"DBZ\")\n", + "displayRate.plot_range_rings([50, 100, 150, 200])\n", + "displayRate.plot_cross_hair(200.)\n", + "\n", + "# KDP (computed)\n", + "\n", + "axKdp = figRate.add_subplot(222)\n", + "displayRate.plot_ppi('KDP', 0, vmin=0, vmax=2.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"KDP (deg/km)\",\n", + " cmap=\"rainbow\")\n", + "displayRate.plot_range_rings([50, 100, 150, 200])\n", + "displayRate.plot_cross_hair(200.)\n", + "\n", + "# RATE_HYBRID (computed)\n", + "\n", + "axHybrid = figRate.add_subplot(223)\n", + "displayRate.plot_ppi('RATE_HYBRID', 0, vmin=0, vmax=50.,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"RATE_HYBRID(mm/hr)\",\n", + " cmap = \"rainbow\")\n", + "displayRate.plot_range_rings([50, 100, 150, 200])\n", + "displayRate.plot_cross_hair(200.)\n", + "\n", + "# NCAR PID (computed)\n", + "\n", + "axPID = figRate.add_subplot(224)\n", + "displayRate.plot_ppi('PID', 0,\n", + " axislabels=(\"x(km)\", \"y(km)\"),\n", + " colorbar_label=\"PID\",\n", + " cmap = \"rainbow\")\n", + "displayRate.plot_range_rings([50, 100, 150, 200])\n", + "displayRate.plot_cross_hair(200.)\n", + "\n", + "pid_cbar = displayRate.cbs[3]\n", + "pid_cbar.set_ticks([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17])\n", + "pid_cbar.set_ticklabels(['cld-drops', 'drizzle', 'lt-rain', 'mod-rain', 'hvy-rain', 'hail', 'rain/hail', 'sm-hail', 'gr/rain', 'dry-snow', 'wet-snow', 'ice', 'irreg-ice', 'slw', 'insects', '2nd-trip', 'clutter'])\n", + "\n", + "figRate.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b6ac4f76", + "metadata": {}, + "source": [ + "## Convert CfRadial polar files to Cartesian" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5493dc86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Program 'Radx2Grid'\n", + "Run-time 2022/08/23 23:21:37.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Running Radx2Grid in ARCHIVE mode\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.427881\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.000161\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.070613\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000639\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000113\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.3e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.263049\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.4e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.48291\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.1e-05\n", + "TIMING, task: Interpolating, secs used: 0.348963\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.9e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220003.nc\n", + "TIMING, task: Writing output files, secs used: 1.09314\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.875567\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.212024\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.075707\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000728\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000139\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.8e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.268008\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 7e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.476379\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6e-05\n", + "TIMING, task: Interpolating, secs used: 0.342659\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.5e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220448.nc\n", + "TIMING, task: Writing output files, secs used: 1.05689\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.560678\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.204119\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.043359\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.00058\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 8.9e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.1e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.216917\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 8.5e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.52201\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6e-05\n", + "TIMING, task: Interpolating, secs used: 0.356129\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.5e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220935.nc\n", + "TIMING, task: Writing output files, secs used: 1.05985\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.837641\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.271269\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.031681\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000695\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000146\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 4e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.152664\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 9.1e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.488319\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6e-05\n", + "TIMING, task: Interpolating, secs used: 0.332071\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.7e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc\n", + "TIMING, task: Writing output files, secs used: 1.00568\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.746694\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.217772\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.096113\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.002184\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 9.5e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.1e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.187239\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.8e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.47635\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.1e-05\n", + "TIMING, task: Interpolating, secs used: 0.366524\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.9e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221906.nc\n", + "TIMING, task: Writing output files, secs used: 1.04584\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.659287\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.218714\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.017567\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000813\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 9e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.9e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.331606\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 0.000143\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.525571\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.9e-05\n", + "TIMING, task: Interpolating, secs used: 0.362195\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.4e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222350.nc\n", + "TIMING, task: Writing output files, secs used: 1.02905\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.71021\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.198376\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.058292\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000612\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 8.7e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.203407\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 8.8e-05\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TIMING, task: Computing grid relative to radar, secs used: 0.488996\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 4.4e-05\n", + "TIMING, task: Interpolating, secs used: 0.353949\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.3e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222834.nc\n", + "TIMING, task: Writing output files, secs used: 1.05065\n", + "======================================================================\n", + "Program 'Radx2Grid'\n", + "Run-time 2022/08/23 23:22:00.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Running Radx2Grid in ARCHIVE mode\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.430818\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.000202\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.072809\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000605\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000149\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3.9e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.282219\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 8.6e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.477655\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.7e-05\n", + "TIMING, task: Interpolating, secs used: 0.358848\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.3e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc\n", + "TIMING, task: Writing output files, secs used: 1.27638\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 1.34881\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.300112\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.079439\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000959\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 9.7e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.264823\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 0.00017\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.480747\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.1e-05\n", + "TIMING, task: Interpolating, secs used: 0.347101\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.1e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220723.nc\n", + "TIMING, task: Writing output files, secs used: 1.04909\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.64048\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.207617\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.023841\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000785\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000111\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3.7e-05\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TIMING, task: Filling search matrix, secs used: 0.188561\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.5e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.467388\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.4e-05\n", + "TIMING, task: Interpolating, secs used: 0.36867\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 6e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221204.nc\n", + "TIMING, task: Writing output files, secs used: 1.04832\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 1.02348\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.269623\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.037337\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000753\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000105\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.1e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.238585\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 0.000155\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.493056\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 8.1e-05\n", + "TIMING, task: Interpolating, secs used: 0.360961\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.9e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc\n", + "TIMING, task: Writing output files, secs used: 1.06419\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.729386\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.202604\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.035863\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000579\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000107\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.164651\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 9e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.499381\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.8e-05\n", + "TIMING, task: Interpolating, secs used: 0.365549\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.9e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222102.nc\n", + "TIMING, task: Writing output files, secs used: 1.04405\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.638909\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.199421\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.015681\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 3.91113\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000623\n", + " _searchRadiusEl: 5.80113\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000104\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.2e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.262799\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 0.000154\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.506552\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.3e-05\n", + "TIMING, task: Interpolating, secs used: 0.39216\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.9e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc\n", + "TIMING, task: Writing output files, secs used: 1.04926\n", + "======================================================================\n", + "Program 'Radx2Grid'\n", + "Run-time 2022/08/23 23:22:21.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Running Radx2Grid in ARCHIVE mode\n", + " Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.429127\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.000154\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.071262\n", + " _scanDeltaAz: 0.6\n", + " _scanDeltaEl: 2.02148\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000581\n", + " _searchRadiusEl: 3.91148\n", + " _searchRadiusAz: 2.49\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 25\n", + "TIMING, task: Computing search limits, secs used: 9e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.163454\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 9.7e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.512284\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6e-05\n", + "TIMING, task: Interpolating, secs used: 0.344006\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220000.nc\n", + "TIMING, task: Writing output files, secs used: 1.14178\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.815143\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.23147\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.07918\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 2.46094\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000955\n", + " _searchRadiusEl: 4.35094\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000116\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3.4e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.184741\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 0.000104\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.515665\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.3e-05\n", + "TIMING, task: Interpolating, secs used: 0.327747\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.6e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220430.nc\n", + "TIMING, task: Writing output files, secs used: 1.05129\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.637002\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.219336\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.038648\n", + " _scanDeltaAz: 0.6\n", + " _scanDeltaEl: 2.63672\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000637\n", + " _searchRadiusEl: 4.52672\n", + " _searchRadiusAz: 2.49\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 25\n", + "TIMING, task: Computing search limits, secs used: 9.8e-05\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 4e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.126254\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 9.2e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.496455\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.8e-05\n", + "TIMING, task: Interpolating, secs used: 0.335073\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.7e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220921.nc\n", + "TIMING, task: Writing output files, secs used: 1.17174\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.864127\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.250427\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.023634\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 2.46094\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000582\n", + " _searchRadiusEl: 4.35094\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000111\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.9e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.085372\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 7.6e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.502792\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.8e-05\n", + "TIMING, task: Interpolating, secs used: 0.335529\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.6e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc\n", + "TIMING, task: Writing output files, secs used: 1.00155\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.58054\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.269445\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.015556\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 2.63672\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000561\n", + " _searchRadiusEl: 4.52672\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.0001\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.5e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.110545\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 8.5e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.496601\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 6.8e-05\n", + "TIMING, task: Interpolating, secs used: 0.332723\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.5e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222051.nc\n", + "TIMING, task: Writing output files, secs used: 0.98031\n", + "INFO - Radx2Grid::_processFile\n", + " Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc\n", + " Reading in file ...\n", + "TIMING, task: Cart interp - reading data, secs used: 0.523014\n", + "TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.19569\n", + "TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.015558\n", + " _scanDeltaAz: 1\n", + " _scanDeltaEl: 2.46094\n", + " _isSector: 0\n", + " _spansNorth: N\n", + "TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.00065\n", + " _searchRadiusEl: 4.35094\n", + " _searchRadiusAz: 2.89\n", + " _searchMinAz: 0\n", + " _searchNAz: 3801\n", + " _searchMaxDistAz: 29\n", + "TIMING, task: Computing search limits, secs used: 0.000107\n", + " Filling search matrix ... \n", + "TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2e-05\n", + "TIMING, task: Filling search matrix, secs used: 0.101267\n", + " Computing grid relative to radar ... \n", + "TIMING, task: Cart interp - before _computeGridRelative, secs used: 9e-05\n", + "TIMING, task: Computing grid relative to radar, secs used: 0.523245\n", + " Interpolating ... \n", + "TIMING, task: Cart interp - before doInterp, secs used: 5.2e-05\n", + "TIMING, task: Interpolating, secs used: 0.345719\n", + "TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.4e-05\n", + " Writing output file ... \n", + " Adding field: RATE_HYBRID\n", + " Adding field: PID\n", + " Adding field: DBZ\n", + " Adding field: range\n", + " Adding field: Coverage\n", + "Mdv2NcfTrans::addGlobalAttributes()\n", + "Mdv2NcfTrans::addDimensions()\n", + "Mdv2NetCDF::_addTimeVariables()\n", + "Mdv2NcfTrans::addCoordinateVariables()\n", + "Mdv2NcfTrans::addFieldVariables()\n", + "adding field: RATE_HYBRID\n", + "NcfFieldData::_setChunking()\n", + " Field: RATE_HYBRID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: PID\n", + "NcfFieldData::_setChunking()\n", + " Field: PID\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: DBZ\n", + "NcfFieldData::_setChunking()\n", + " Field: DBZ\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: range\n", + "NcfFieldData::_setChunking()\n", + " Field: range\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "adding field: Coverage\n", + "NcfFieldData::_setChunking()\n", + " Field: Coverage\n", + " nyChunk: 460\n", + " nxChunk: 460\n", + "Mdv2NcfTrans::_putTimeVariables()\n", + "Mdv2NcfTrans::_putCoordinateVariables()\n", + "Mdv2NcfTrans::_putFieldDataVariables()\n", + "OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc\n", + "TIMING, task: Writing output files, secs used: 0.969649\n" + ] + } + ], + "source": [ + "# Run Radx2Grid for 3 NEXRAD radars\n", + "\n", + "for radar_name in ['KGLD', 'KUEX', 'KDDC']:\n", + " # Set radar in name environment variable\n", + " os.environ['RADAR_NAME'] = radar_name\n", + " # Run RadxRate using param file\n", + " !/usr/local/lrose/bin/Radx2Grid -params ./params/Radx2Grid.rate -debug -start \"2021 07 06 22 00 00\" -end \"2021 07 06 22 30 00\"" + ] + }, + { + "cell_type": "markdown", + "id": "b0652028", + "metadata": {}, + "source": [ + "## List files created by Radx2Grid" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5247b42a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706:\r\n", + "ncf_20210706_220000.nc\tncf_20210706_220921.nc\tncf_20210706_222051.nc\r\n", + "ncf_20210706_220430.nc\tncf_20210706_221600.nc\tncf_20210706_222533.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706:\r\n", + "ncf_20210706_220003.nc\tncf_20210706_221420.nc\tncf_20210706_222834.nc\r\n", + "ncf_20210706_220448.nc\tncf_20210706_221906.nc\r\n", + "ncf_20210706_220935.nc\tncf_20210706_222350.nc\r\n", + "\r\n", + "/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706:\r\n", + "ncf_20210706_220249.nc\tncf_20210706_221204.nc\tncf_20210706_222102.nc\r\n", + "ncf_20210706_220723.nc\tncf_20210706_221633.nc\tncf_20210706_222531.nc\r\n" + ] + } + ], + "source": [ + "# List the Cartesian files created by Radx2Grid\n", + "!ls -R ${NEXRAD_DATA_DIR}/mdv/radarCart/K*/2*" + ] + }, + { + "cell_type": "markdown", + "id": "67c2cadb", + "metadata": {}, + "source": [ + "## Merge Cartesian files into mosaic, using MdvMerge2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4aa8a835", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Program 'MdvMerge2'\n", + "Run-time 2022/08/23 23:22:53.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Archive trigger\n", + " Start time: 2021/07/06 22:00:00\n", + " End time: 2021/07/06 22:30:00\n", + " Prev time: 2021/07/06 21:55:00\n", + " Next time: 2021/07/06 22:00:00\n", + "Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Field names: DBZ RATE_HYBRID PID range\n", + "Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Field names: DBZ RATE_HYBRID PID range\n", + "Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Field names: DBZ RATE_HYBRID PID range\n", + " Next trigger: 2021/07/06 22:00:00\n", + "----> Trigger time: 2021/07/06 22:00:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:00:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220003.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:00:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:00:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220000.nc\n", + "Writing merged MDV file, time 2021/07/06 22:00:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220000.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:05:00\n", + "----> Trigger time: 2021/07/06 22:05:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:05:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220448.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:05:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:05:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220430.nc\n", + "Writing merged MDV file, time 2021/07/06 22:05:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220500.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:10:00\n", + "----> Trigger time: 2021/07/06 22:10:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:10:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220935.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:10:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221204.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:10:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220921.nc\n", + "Writing merged MDV file, time 2021/07/06 22:10:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221000.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:15:00\n", + "----> Trigger time: 2021/07/06 22:15:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:15:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:15:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:15:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc\n", + "Writing merged MDV file, time 2021/07/06 22:15:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:20:00\n", + "----> Trigger time: 2021/07/06 22:20:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:20:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221906.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:20:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222102.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:20:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222051.nc\n", + "Writing merged MDV file, time 2021/07/06 22:20:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222000.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:25:00\n", + "----> Trigger time: 2021/07/06 22:25:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:25:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222350.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:25:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:25:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc\n", + "Writing merged MDV file, time 2021/07/06 22:25:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222500.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:30:00\n", + "----> Trigger time: 2021/07/06 22:30:00\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:30:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222834.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:30:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc\n", + "READ_CLOSEST\n", + "Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + "Mdvx read request\n", + "-----------------\n", + " Encoding type: ENCODING_FLOAT32 (FLOAT)\n", + " Compression type: COMPRESSION_NONE\n", + " Scaling type: SCALING_ROUNDED\n", + " Composite?: 0\n", + " FillMissing?: 0\n", + " Field names: DBZ, RATE_HYBRID, PID, range\n", + " FieldFileHeaders?: 0\n", + " Search mode: READ_CLOSEST\n", + " Search time: 2021/07/06 22:30:00\n", + " Search margin: 600 secs\n", + " Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read32BitHeaders?: false\n", + " Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC\n", + " Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc\n", + "Writing merged MDV file, time 2021/07/06 22:30:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_223000.mdv.cf.nc\n", + " Next trigger: 2021/07/06 22:35:00\n", + " Done\n" + ] + } + ], + "source": [ + "# Run MdvMerge2 to merrge the Cart data from 3 radars into a mosaic\n", + "!/usr/local/lrose/bin/MdvMerge2 -params params/MdvMerge2.mosaic -start \"2021 07 06 22 00 00\" -end \"2021 07 06 22 30 00\" -debug" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a1992c17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706:\r\n", + "20210706_220000.mdv.cf.nc 20210706_221500.mdv.cf.nc 20210706_223000.mdv.cf.nc\r\n", + "20210706_220500.mdv.cf.nc 20210706_222000.mdv.cf.nc\r\n", + "20210706_221000.mdv.cf.nc 20210706_222500.mdv.cf.nc\r\n" + ] + } + ], + "source": [ + "# List the mosaic files created by MdvMerge2\n", + "!ls -R ${NEXRAD_DATA_DIR}/mdv/radarCart/mosaic/2*" + ] + }, + { + "cell_type": "markdown", + "id": "4c7abd61", + "metadata": {}, + "source": [ + "## Read in file from radar mosaic" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2f44f6b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radar mosaic file path: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc\n", + "Radar mosaic data set: \n", + "root group (NETCDF4 data model, file format HDF5):\n", + " Conventions: CF-1.6\n", + " history: Data merged from following files:\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc\n", + "\n", + " source: NEXRAD radars\n", + " title: 3D RADAR MOSAIC\n", + " comment: \n", + " dimensions(sizes): time(1), bounds(2), x0(900), y0(700), z0(31)\n", + " variables(dimensions): float64 time(time), float64 start_time(time), float64 stop_time(time), float64 time_bounds(time, bounds), float32 x0(x0), float32 y0(y0), float32 z0(z0), int32 grid_mapping_0(), int32 mdv_master_header(time), int16 DBZ(time, z0, y0, x0), int16 RATE_HYBRID(time, z0, y0, x0), int16 PID(time, z0, y0, x0), int16 range(time, z0, y0, x0)\n", + " groups: \n", + "Start time: 2021/07/06-16:14:20 UTC\n", + "minLonMosaic, maxLonMosaic: -104.50500106811523 -95.50500106811523\n", + "minLatMosaic, maxLatMosaic: 35.4950008392334 42.49499702453613\n", + "minHt, maxHt: 1.25 20.0\n", + "hts: [ 1.25 1.5 1.75 2. 2.25 2.5 2.75 3. 3.5 4. 4.5 5.\n", + " 5.5 6. 6.5 7. 7.5 8. 8.5 9. 10. 11. 12. 13.\n", + " 14. 15. 16. 17. 18. 19. 20. ]\n", + "Shape of composite DBZ grid: (700, 900)\n" + ] + } + ], + "source": [ + "# Read in example radar mosaic for a single time\n", + "\n", + "filePathMosaic = os.path.join(nexradDataDir, 'mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc')\n", + "dsMosaic = nc.Dataset(filePathMosaic)\n", + "print(\"Radar mosaic file path: \", filePathMosaic)\n", + "print(\"Radar mosaic data set: \", dsMosaic)\n", + "\n", + "# Compute time\n", + "\n", + "uTimeSecs = dsMosaic['start_time'][0]\n", + "startTime = datetime.datetime.fromtimestamp(int(uTimeSecs))\n", + "startTimeStr = startTime.strftime('%Y/%m/%d-%H:%M:%S UTC')\n", + "print(\"Start time: \", startTimeStr)\n", + "\n", + "# print(dsMosaic['DBZ'])\n", + "#for dim in dsMosaic.dimensions.values():\n", + "# print(dim)\n", + "#for var in dsMosaic.variables.values():\n", + "# print(\"========================================\")\n", + "# print(var)\n", + "# print(\"========================================\")\n", + "\n", + "# create 3D dbz array with nans for missing vals\n", + "\n", + "dsDbz = dsMosaic['DBZ']\n", + "dbz3D = np.array(dsDbz)\n", + "fillValue = dsDbz._FillValue\n", + "# print(\"fillValue: \", fillValue)\n", + "\n", + "# if 4D (i.e. time is dim0) change to 3D\n", + "\n", + "if (len(dbz3D.shape) == 4):\n", + " dbz3D = dbz3D[0]\n", + "\n", + "# Compute mosaic grid limits\n", + "\n", + "(nZMosaic, nYMosaic, nXMosaic) = dbz3D.shape\n", + "lon = np.array(dsMosaic['x0'])\n", + "lat = np.array(dsMosaic['y0'])\n", + "ht = np.array(dsMosaic['z0'])\n", + "dLonMosaic = lon[1] - lon[0]\n", + "dLatMosaic = lat[1] - lat[0]\n", + "minLonMosaic = lon[0] - dLonMosaic / 2.0\n", + "maxLonMosaic = lon[-1] + dLonMosaic / 2.0\n", + "minLatMosaic = lat[0] - dLatMosaic / 2.0\n", + "maxLatMosaic = lat[-1] + dLatMosaic / 2.0\n", + "minHtMosaic = ht[0]\n", + "maxHtMosaic = ht[-1]\n", + "\n", + "print(\"minLonMosaic, maxLonMosaic: \", minLonMosaic, maxLonMosaic)\n", + "print(\"minLatMosaic, maxLatMosaic: \", minLatMosaic, maxLatMosaic)\n", + "print(\"minHt, maxHt: \", minHtMosaic, maxHtMosaic)\n", + "print(\"hts: \", ht)\n", + "del lon, lat, ht\n", + "\n", + "# Compute column-max reflectivity\n", + "\n", + "dbzPlaneMax = np.amax(dbz3D, (0))\n", + "dbzPlaneMax[dbzPlaneMax == fillValue] = np.nan\n", + "\n", + "print(\"Shape of composite DBZ grid: \", dbzPlaneMax.shape)\n", + "#print(dbzPlaneMax[dbzPlaneMax != np.nan])\n", + "#print(np.min(dbzPlaneMax[dbzPlaneMax != np.nan]))\n", + "#print(np.max(dbzPlaneMax))\n" + ] + }, + { + "cell_type": "markdown", + "id": "376da301", + "metadata": {}, + "source": [ + "### Create map for Cartesian grid plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "895b9db7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create map for plotting lat/lon grids\n", + "def new_map(fig):\n", + " \n", + " ## Create projection centered on data\n", + " proj = ccrs.PlateCarree()\n", + "\n", + " ## New axes with the specified projection:\n", + " ax = fig.add_subplot(1, 1, 1, projection=proj)\n", + " \n", + " ## Set extent the same as radar mosaic\n", + " ax.set_extent([minLonMosaic, maxLonMosaic, minLatMosaic, maxLatMosaic])\n", + "\n", + " ## Add grid lines & labels:\n", + " gl = ax.gridlines( crs=ccrs.PlateCarree()\n", + " , draw_labels=True\n", + " , linewidth=1\n", + " , color='lightgray'\n", + " , alpha=0.5, linestyle='--'\n", + " ) \n", + " gl.top_labels = False\n", + " gl.left_labels = True\n", + " gl.right_labels = False\n", + " gl.xlines = True\n", + " gl.ylines = True\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " gl.xlabel_style = {'size': 8, 'weight': 'bold'}\n", + " gl.ylabel_style = {'size': 8, 'weight': 'bold'}\n", + " \n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "id": "b92dc28f", + "metadata": {}, + "source": [ + "### Plot column-max reflectivity in plan view, and a N/S and W/E cross section of reflectivity" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c3d9b6c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Radar mosaic column-max DBZ: 2021/07/06-16:14:20 UTC')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot column-max reflectivity\n", + "figDbzComp = plt.figure(figsize=(8, 8), dpi=150)\n", + "axDbzComp = new_map(figDbzComp)\n", + "plt.imshow(dbzPlaneMax,\n", + " cmap='pyart_Carbone42',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLonMosaic, maxLonMosaic, minLatMosaic, maxLatMosaic))\n", + "axDbzComp.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='black')\n", + "axDbzComp.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", + "#axDbzComp.coastlines('10m', 'darkgray', linewidth=1, zorder=0)\n", + "plt.colorbar(label=\"DBZ\", orientation=\"vertical\", shrink=0.5)\n", + "plt.title(\"Radar mosaic column-max DBZ: \" + startTimeStr)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4eb7d3d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dbzVertWE.shape: (31, 900)\n", + "dbzVertWEMax.shape: (31, 900)\n", + "dbzVertNS.shape: (31, 700)\n", + "dbzVertNSMax.shape: (31, 700)\n" + ] + } + ], + "source": [ + "# Compute W-E DBZ vertical section\n", + "nYHalfMosaic = int(nYMosaic/2)\n", + "dbzVertWE = dbz3D[:, nYHalfMosaic:(nYHalfMosaic+1), :]\n", + "dbzVertWE = dbzVertWE.reshape(dbzVertWE.shape[0], dbzVertWE.shape[2])\n", + "print('dbzVertWE.shape: ', dbzVertWE.shape)\n", + "dbzVertWE[dbzVertWE == fillValue] = np.nan\n", + "dbzVertWEMax = np.amax(dbz3D, axis=1)\n", + "dbzVertWEMax[dbzVertWEMax == fillValue] = np.nan\n", + "print('dbzVertWEMax.shape: ', dbzVertWEMax.shape)\n", + "\n", + "# Compute DBZ N-S vertical section\n", + "nXHalfMosaic = int(nXMosaic/2)\n", + "dbzVertNS = dbz3D[:, :, nXHalfMosaic:(nXHalfMosaic+1)]\n", + "dbzVertNS = dbzVertNS.reshape(dbzVertNS.shape[0], dbzVertNS.shape[1])\n", + "dbzVertNS[dbzVertNS == fillValue] = np.nan\n", + "print('dbzVertNS.shape: ', dbzVertNS.shape)\n", + "dbzVertNSMax = np.amax(dbz3D, axis=2)\n", + "dbzVertNSMax[dbzVertNSMax == fillValue] = np.nan\n", + "print('dbzVertNSMax.shape: ', dbzVertNSMax.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "00c09a1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot mid W-E DBZ vertical section\n", + "\n", + "fig2 = plt.figure(num=2, figsize=[12, 8], layout='constrained')\n", + "ax2a = fig2.add_subplot(211, xlim = (minLonMosaic, maxLonMosaic), ylim = (minHtMosaic, maxHtMosaic))\n", + "plt.imshow(dbzVertWE,\n", + " cmap='pyart_Carbone42',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLonMosaic, maxLonMosaic, minHtMosaic, maxHtMosaic))\n", + "ax2a.set_aspect(0.15)\n", + "ax2a.set_xlabel('Longitude (deg)')\n", + "ax2a.set_ylabel('Height (km)')\n", + "plt.title(\"Vert slice mid W-E DBZ: \" + startTimeStr)\n", + "\n", + "# Plot mid N-S DBZ vertical section\n", + "\n", + "ax2b = fig2.add_subplot(212, xlim = (minLatMosaic, maxLatMosaic), ylim = (minHtMosaic, maxHtMosaic))\n", + "plt.imshow(dbzVertNS,\n", + " cmap='pyart_Carbone42',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLatMosaic, maxLatMosaic, minHtMosaic, maxHtMosaic))\n", + "ax2b.set_aspect(0.15)\n", + "ax2b.set_xlabel('Latitude (deg)')\n", + "ax2b.set_ylabel('Height (km)')\n", + "plt.title(\"Vert slice mid NS DBZ: \" + startTimeStr)\n", + "\n", + "plt.colorbar(label=\"DBZ\", orientation=\"horizontal\", fraction=0.1)" + ] + }, + { + "cell_type": "markdown", + "id": "d0b71e8e", + "metadata": {}, + "source": [ + "### View the Ecco parameter file\n", + "\n", + "The paths of the input files, and the output results file, are specified in the parameters.\n", + "\n", + "Also included are all of the parameters used to control the algorithm.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f44d69ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/**********************************************************************\r\n", + " * TDRP params for Ecco\r\n", + " **********************************************************************/\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// Program name: Ecco.\r\n", + "//\r\n", + "// Ecco finds convective and stratiform regions within a Cartesian radar \r\n", + "// volume.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "//======================================================================\r\n", + "//\r\n", + "// PROCESS CONTROL.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// debug ///////////////////////////////////\r\n", + "//\r\n", + "// Debug option.\r\n", + "//\r\n", + "// If set, debug messages will be printed appropriately.\r\n", + "//\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// DEBUG_OFF\r\n", + "// DEBUG_NORM\r\n", + "// DEBUG_VERBOSE\r\n", + "// DEBUG_EXTRA\r\n", + "//\r\n", + "\r\n", + "debug = DEBUG_OFF;\r\n", + "\r\n", + "///////////// instance ////////////////////////////////\r\n", + "//\r\n", + "// Process instance.\r\n", + "//\r\n", + "// Used for registration with procmap.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "instance = \"nexrad_mosaic\";\r\n", + "\r\n", + "///////////// mode ////////////////////////////////////\r\n", + "//\r\n", + "// Operating mode.\r\n", + "//\r\n", + "// In REALTIME mode, the program waits for a new input file. In ARCHIVE \r\n", + "// mode, it moves through the data between the start and end times set \r\n", + "// on the command line. In FILELIST mode, it moves through the list of \r\n", + "// file names specified on the command line.\r\n", + "//\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// ARCHIVE\r\n", + "// REALTIME\r\n", + "// FILELIST\r\n", + "//\r\n", + "\r\n", + "mode = ARCHIVE;\r\n", + "\r\n", + "///////////// use_multiple_threads ////////////////////\r\n", + "//\r\n", + "// Option to use multiple threads for speed.\r\n", + "//\r\n", + "// Computing the texture is the most time consuming step. If this is \r\n", + "// true, then the texture will be computer for each vertical level in a \r\n", + "// separate thread, in parallel. This speeds up the processing. If this \r\n", + "// is false, the threads will be called serially. This is useful for \r\n", + "// debugging.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "use_multiple_threads = TRUE;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// DATA INPUT.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// input_url ///////////////////////////////\r\n", + "//\r\n", + "// URL for input data.\r\n", + "//\r\n", + "// This is used in REALTIME and ARCHIVE modes only. In FILELIST mode, \r\n", + "// the file paths are specified on the command line.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "input_url = \"$(NEXRAD_DATA_DIR)/mdv/radarCart/mosaic\";\r\n", + "\r\n", + "///////////// dbz_field_name //////////////////////////\r\n", + "//\r\n", + "// dBZ field name in input MDV files.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "dbz_field_name = \"DBZ\";\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// ALGORITHM PARAMETERS.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// min_valid_height ////////////////////////\r\n", + "//\r\n", + "// Min height used in analysis (km).\r\n", + "//\r\n", + "// Only data at or above this altitude is used.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_valid_height = 0;\r\n", + "\r\n", + "///////////// max_valid_height ////////////////////////\r\n", + "//\r\n", + "// Max height used in analysis (km).\r\n", + "//\r\n", + "// Only data at or below this altitude is used.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "max_valid_height = 25;\r\n", + "\r\n", + "///////////// min_valid_dbz ///////////////////////////\r\n", + "//\r\n", + "// Minimum reflectivity threshold for this analysis (dBZ).\r\n", + "//\r\n", + "// Reflectivity below this threshold is set to missing.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_valid_dbz = 0;\r\n", + "\r\n", + "///////////// base_dbz ////////////////////////////////\r\n", + "//\r\n", + "// Set base DBZ value.\r\n", + "//\r\n", + "// Before computing the texture, we subtract the baseDBZ from the \r\n", + "// measured DBZ. This adjusts the DBZ values into the positive range. \r\n", + "// For S-, C- and X-band radars, this can be set to 0 dBZ, which is the \r\n", + "// default. For Ka-band radars this should be around -10 dBZ. For W-band \r\n", + "// radars -20 dBZ is appropriate.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "base_dbz = 0;\r\n", + "\r\n", + "///////////// min_valid_volume_for_convective /////////\r\n", + "//\r\n", + "// Min volume of a convective region (km3).\r\n", + "//\r\n", + "// Regions of smaller volume will be labeled SMALL.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_valid_volume_for_convective = 20;\r\n", + "\r\n", + "///////////// min_vert_extent_for_convective //////////\r\n", + "//\r\n", + "// Min vertical echo extent of a convective region (km).\r\n", + "//\r\n", + "// The vertical extent is computed as the mid height of the top layer in \r\n", + "// the echo minus the mid height of the bottom layer. For an echo that \r\n", + "// exists in only one layer, the vertical extent would therefore be \r\n", + "// zero. This parameter lets us require that a valid convective echo \r\n", + "// exist in multiple layers, which is desirable and helps to remove \r\n", + "// spurious echoes as candidates for convection.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_vert_extent_for_convective = 3;\r\n", + "\r\n", + "///////////// dbz_for_echo_tops ///////////////////////\r\n", + "//\r\n", + "// Reflectivity for determing echo tops.\r\n", + "//\r\n", + "// Echo tops are defined as the max ht with reflectivity at or above \r\n", + "// this value.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "dbz_for_echo_tops = 18;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// COMPUTING REFLECTIVITY TEXTURE.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// texture_radius_km ///////////////////////\r\n", + "//\r\n", + "// Radius for texture analysis (km).\r\n", + "//\r\n", + "// We determine the reflectivity 'texture' at a point by computing the \r\n", + "// standard deviation of the square of the reflectivity, for all grid \r\n", + "// points within this radius of the central point. We then compute the \r\n", + "// square root of that sdev.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "texture_radius_km = 7;\r\n", + "\r\n", + "///////////// min_valid_fraction_for_texture //////////\r\n", + "//\r\n", + "// Minimum fraction of surrounding points for texture computations.\r\n", + "//\r\n", + "// For a valid computation of texture, we require at least this fraction \r\n", + "// of points around the central point to have valid reflectivity.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_valid_fraction_for_texture = 0.25;\r\n", + "\r\n", + "///////////// min_valid_fraction_for_fit //////////////\r\n", + "//\r\n", + "// Minimum fraction of surrounding points for 2D fit to DBZ.\r\n", + "//\r\n", + "// We compute a 2D fit to the reflectivity around a grid point, to \r\n", + "// remove any systematic gradient. For a valid fit, we require at least \r\n", + "// this fraction of points around the central point to have valid \r\n", + "// reflectivity.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_valid_fraction_for_fit = 0.67;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// CONVERTING REFLECTIVITY TEXTURE TO CONVECTIVITY.\r\n", + "//\r\n", + "// Convectivity ranges from 0 to 1. To convert texture to convectivity, \r\n", + "// we apply a piece-wise linear transfer function. This section defines \r\n", + "// the lower texture limit and the upper texture limit. At or below the \r\n", + "// lower limit convectivity is set to 0. At or above the upper limit \r\n", + "// convectivity is set to 1. Between these two limits convectivity \r\n", + "// varies linearly with texture.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// texture_limit_low ///////////////////////\r\n", + "//\r\n", + "// Lower limit for texture.\r\n", + "//\r\n", + "// Below this texture the convectivity is set to 0.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "texture_limit_low = 0;\r\n", + "\r\n", + "///////////// texture_limit_high //////////////////////\r\n", + "//\r\n", + "// Upper limit for texture.\r\n", + "//\r\n", + "// Above this texture the convectivity is set to 1. Between the limits \r\n", + "// convectivity varies linearly with texture.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "texture_limit_high = 30;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// SETTING CONVECTIVE OR STRATIFORM FLAGS BASED ON CONVECTIVITY.\r\n", + "//\r\n", + "// If neither is set, we flag the point as MIXED.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// min_convectivity_for_convective /////////\r\n", + "//\r\n", + "// Minimum convectivity for convective at a point.\r\n", + "//\r\n", + "// If the convectivity at a point exceeds this value, we set the \r\n", + "// convective flag at this point.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_convectivity_for_convective = 0.5;\r\n", + "\r\n", + "///////////// max_convectivity_for_stratiform /////////\r\n", + "//\r\n", + "// Maximum convectivity for stratiform at a point.\r\n", + "//\r\n", + "// If the convectivity at a point is less than this value, we set the \r\n", + "// stratiform flag at this point. If it is above this but less than \r\n", + "// min_convectivity_for_convective we flag the point as MIXED.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "max_convectivity_for_stratiform = 0.4;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// DETERMINING ADVANCED ECHO TYPE USING CLUMPING AND TEMPERATURE.\r\n", + "//\r\n", + "// We performing clumping on the convectivity field to identify \r\n", + "// convective entities as objects. The main threshold used for the \r\n", + "// clumping is min_convectivity_for_convective. By default a secondary \r\n", + "// threshold is also used - see below.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// clumping_use_dual_thresholds ////////////\r\n", + "//\r\n", + "// Option to use dual thresholds to better identify convective clumps.\r\n", + "//\r\n", + "// NOTE: this step is performed in 2D. If set, the clumping is performed \r\n", + "// in two stages. First, an outer convectivity envelope is computed, \r\n", + "// using min_convectivity_for_convective. Then, using the parameters \r\n", + "// below, for each clump a search is performed for sub-clumps within the \r\n", + "// envelope of the main clump, suing the secondary threshold. If there \r\n", + "// is only one sub-clump, the original clump is used unchanged. If there \r\n", + "// are two or more valid sub-clumps, based on the parameters below, \r\n", + "// these sub-clumps are progrresively grown to where they meet, or to \r\n", + "// the original clump envelope. The final 3D clumps are computed by \r\n", + "// breaking the original clump into regions based upon these secondary \r\n", + "// 2D areas.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "clumping_use_dual_thresholds = TRUE;\r\n", + "\r\n", + "///////////// clumping_secondary_convectivity /////////\r\n", + "//\r\n", + "// Secondary convectivity threshold for clumping.\r\n", + "//\r\n", + "// We use the secondary threshold to find sub-clumps within the envelope \r\n", + "// of each original clump.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "clumping_secondary_convectivity = 0.65;\r\n", + "\r\n", + "///////////// all_subclumps_min_area_fraction /////////\r\n", + "//\r\n", + "// Min area of all sub-clumps, as a fraction of the original clump area.\r\n", + "//\r\n", + "// We sum the areas of the sub-clumps, and compute the fraction relative \r\n", + "// to the area of the original clump. For the sub-clumps to be valid, \r\n", + "// the computed fraction must exceed this parameter.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "all_subclumps_min_area_fraction = 0.33;\r\n", + "\r\n", + "///////////// each_subclump_min_area_fraction /////////\r\n", + "//\r\n", + "// Min area of each valid sub-clump, as a fraction of the original \r\n", + "// clump.\r\n", + "//\r\n", + "// We compute the area of each sub-clump, and compute the fraction \r\n", + "// relative to the area of the original clump. For a subclump to be \r\n", + "// valid, the area fraction must exceed this parameter.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "each_subclump_min_area_fraction = 0.02;\r\n", + "\r\n", + "///////////// each_subclump_min_area_km2 //////////////\r\n", + "//\r\n", + "// Min area of each valid sub-clump (km2).\r\n", + "//\r\n", + "// We compute the area of each sub-clump. For a subclump to be valid, \r\n", + "// the area must exceed this parameter.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "each_subclump_min_area_km2 = 2;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// SPECIFYING VERTICAL LEVELS FOR ADVANCED ECHO TYPE - TEMPERATURE or \r\n", + "// HEIGHT?.\r\n", + "//\r\n", + "// We need to specify the vertical separation between shallow, mid-level \r\n", + "// and high clouds. We use the freezing level to separate warm clouds \r\n", + "// and cold clouds. And we use the divergence level to separate the \r\n", + "// mid-level clouds from high-level clouds such as anvil. These vertical \r\n", + "// limits can be specified as heights MSL (in km), or as temperatures. \r\n", + "// If temperatures are used, we read in the temperature profile from a \r\n", + "// model.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// vert_levels_type ////////////////////////\r\n", + "//\r\n", + "// How we specify the vertical levels.\r\n", + "//\r\n", + "// If temperatures are used, we need to read in the temperature profile \r\n", + "// from a model.\r\n", + "//\r\n", + "//\r\n", + "// Type: enum\r\n", + "// Options:\r\n", + "// VERT_LEVELS_BY_TEMP\r\n", + "// VERT_LEVELS_BY_HT\r\n", + "//\r\n", + "\r\n", + "vert_levels_type = VERT_LEVELS_BY_TEMP;\r\n", + "\r\n", + "///////////// temp_profile_url ////////////////////////\r\n", + "//\r\n", + "// URL for temperature profile data, in MDV/Netcdf-CF format.\r\n", + "//\r\n", + "// We read in the model data that is closest in time to the reflectivity \r\n", + "// data.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "temp_profile_url = \"$(NEXRAD_DATA_DIR)/mdv/ruc\";\r\n", + "\r\n", + "///////////// temp_profile_field_name /////////////////\r\n", + "//\r\n", + "// Name of temperature field in the model data. This should be in \r\n", + "// degrees C.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "temp_profile_field_name = \"TMP\";\r\n", + "\r\n", + "///////////// temp_profile_search_margin //////////////\r\n", + "//\r\n", + "// Search margin for finding the temp profile data (secs).\r\n", + "//\r\n", + "// The temp profile must be within this number of seconds of the dbz \r\n", + "// data.\r\n", + "//\r\n", + "//\r\n", + "// Type: int\r\n", + "//\r\n", + "\r\n", + "temp_profile_search_margin = 21600;\r\n", + "\r\n", + "///////////// shallow_threshold_temp //////////////////\r\n", + "//\r\n", + "// Shallow cloud temperature threshold (degC).\r\n", + "//\r\n", + "// Shallow cloud tops are below this temperature. Used if \r\n", + "// vert_levels_type = VERT_LEVELS_BY_TEMP.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "shallow_threshold_temp = 0;\r\n", + "\r\n", + "///////////// deep_threshold_temp /////////////////////\r\n", + "//\r\n", + "// Deep cloud temperature threshold (degC).\r\n", + "//\r\n", + "// Deep clouds extend above this height. Used if vert_levels_type = \r\n", + "// VERT_LEVELS_BY_TEMP.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "deep_threshold_temp = -25;\r\n", + "\r\n", + "///////////// shallow_threshold_ht ////////////////////\r\n", + "//\r\n", + "// Shallow cloud height threshold if temperature is not available (km).\r\n", + "//\r\n", + "// Shallow cloud tops are below this height. Used if vert_levels_type = \r\n", + "// VERT_LEVELS_BY_HT.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "shallow_threshold_ht = 4;\r\n", + "\r\n", + "///////////// deep_threshold_ht ///////////////////////\r\n", + "//\r\n", + "// Deep cloud height threshold if temperature is not available (km).\r\n", + "//\r\n", + "// Deep clouds extend above this height. Used if vert_levels_type = \r\n", + "// VERT_LEVELS_BY_HT.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "deep_threshold_ht = 9;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// DETERMINING ADVANCED CATEGORY FROM CLUMP PROPERTIES.\r\n", + "//\r\n", + "// Based on the temp or height criteria above, we compute the deep, mid \r\n", + "// and shallow convective fractions within each sub-clump. We also \r\n", + "// determine whether there is stratiform echo below the clump. The \r\n", + "// following parameters are then used to determine the deep, elevated, \r\n", + "// mid or shallow echo types for the convection. If a determination is \r\n", + "// not clear, the overall category is set to mixed.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// min_conv_fraction_for_deep //////////////\r\n", + "//\r\n", + "// The minimun convective fraction in the clump for deep convection.\r\n", + "//\r\n", + "// The fraction of deep within the clump must exceed this for an echo \r\n", + "// type of deep.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_conv_fraction_for_deep = 0.05;\r\n", + "\r\n", + "///////////// min_conv_fraction_for_shallow ///////////\r\n", + "//\r\n", + "// The minimun convective fraction in the clump for shallow convection.\r\n", + "//\r\n", + "// The fraction of shallow within the clump must exceed this for an echo \r\n", + "// type of shallow.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_conv_fraction_for_shallow = 0.95;\r\n", + "\r\n", + "///////////// max_shallow_conv_fraction_for_elevated //\r\n", + "//\r\n", + "// The maximum shallow convective fraction in the clump for elevated \r\n", + "// convection.\r\n", + "//\r\n", + "// The fraction of shallow within the clump must be less than this for \r\n", + "// an echo type of elevated.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "max_shallow_conv_fraction_for_elevated = 0.05;\r\n", + "\r\n", + "///////////// max_deep_conv_fraction_for_elevated /////\r\n", + "//\r\n", + "// The maximum deep convective fraction in the clump for elevated \r\n", + "// convection.\r\n", + "//\r\n", + "// The fraction of deep within the clump must be less than this for an \r\n", + "// echo type of elevated.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "max_deep_conv_fraction_for_elevated = 0.25;\r\n", + "\r\n", + "///////////// min_strat_fraction_for_strat_below //////\r\n", + "//\r\n", + "// The minimun area fraction of stratiform echo below the clump to \r\n", + "// determine there is stratiform below.\r\n", + "//\r\n", + "// For elevated convection, we need to determine if there is stratiform \r\n", + "// echo below. For a designation of elevated, this is the minimum \r\n", + "// fraction of the area below the clump that has stratiform echo in the \r\n", + "// plane immediately below it.\r\n", + "//\r\n", + "//\r\n", + "// Type: double\r\n", + "//\r\n", + "\r\n", + "min_strat_fraction_for_strat_below = 0.9;\r\n", + "\r\n", + "//======================================================================\r\n", + "//\r\n", + "// DATA OUTPUT.\r\n", + "//\r\n", + "//======================================================================\r\n", + " \r\n", + "///////////// output_url //////////////////////////////\r\n", + "//\r\n", + "// Output URL.\r\n", + "//\r\n", + "// Output files are written to this URL.\r\n", + "//\r\n", + "//\r\n", + "// Type: string\r\n", + "//\r\n", + "\r\n", + "output_url = \"$(NEXRAD_DATA_DIR)/mdv/ecco/mosaic\";\r\n", + "\r\n", + "///////////// write_partition /////////////////////////\r\n", + "//\r\n", + "// Write out partition fields.\r\n", + "//\r\n", + "// This will write out the 3D, 2D and column-max partition.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_partition = TRUE;\r\n", + "\r\n", + "///////////// write_texture ///////////////////////////\r\n", + "//\r\n", + "// Write out texture fields.\r\n", + "//\r\n", + "// This will write out the 3D and column-max texture.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_texture = TRUE;\r\n", + "\r\n", + "///////////// write_convectivity //////////////////////\r\n", + "//\r\n", + "// Write out convectivity fields.\r\n", + "//\r\n", + "// This will write out the 3D and column-max convectivity.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_convectivity = TRUE;\r\n", + "\r\n", + "///////////// write_3D_dbz ////////////////////////////\r\n", + "//\r\n", + "// Write out 3D dbz field.\r\n", + "//\r\n", + "// This will be an echo of the input field.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_3D_dbz = TRUE;\r\n", + "\r\n", + "///////////// write_col_max_dbz ///////////////////////\r\n", + "//\r\n", + "// Write out column maximum dbz field.\r\n", + "//\r\n", + "// This is the max reflectivity at any height.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_col_max_dbz = TRUE;\r\n", + "\r\n", + "///////////// write_convective_dbz ////////////////////\r\n", + "//\r\n", + "// Write out convective dbz field.\r\n", + "//\r\n", + "// This will write out the 3D convective DBZ field.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_convective_dbz = TRUE;\r\n", + "\r\n", + "///////////// write_tops //////////////////////////////\r\n", + "//\r\n", + "// Write out echo, convective and stratiform tops.\r\n", + "//\r\n", + "// These are 2D fields.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_tops = TRUE;\r\n", + "\r\n", + "///////////// write_fraction_active ///////////////////\r\n", + "//\r\n", + "// Write out 2D field showing fraction active.\r\n", + "//\r\n", + "// This the active fraction in the computational circle.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_fraction_active = TRUE;\r\n", + "\r\n", + "///////////// write_height_grids //////////////////////\r\n", + "//\r\n", + "// Write out 2D field showing shallow and deep heights.\r\n", + "//\r\n", + "// These are based on model temperature.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_height_grids = FALSE;\r\n", + "\r\n", + "///////////// write_temperature ///////////////////////\r\n", + "//\r\n", + "// Write out 3D temperature field.\r\n", + "//\r\n", + "// This comes from a model, remapped onto the reflectivity grid.\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_temperature = TRUE;\r\n", + "\r\n", + "///////////// write_clumping_debug_fields /////////////\r\n", + "//\r\n", + "// Option to write fields to the output files for debugging the dual \r\n", + "// threshold clumping.\r\n", + "//\r\n", + "// If this is set, the following debug fields are written to the output \r\n", + "// files: .\r\n", + "//\r\n", + "//\r\n", + "// Type: boolean\r\n", + "//\r\n", + "\r\n", + "write_clumping_debug_fields = FALSE;\r\n", + "\r\n" + ] + } + ], + "source": [ + "# View the param file\n", + "!cat ./params/Ecco.nexrad_mosaic" + ] + }, + { + "cell_type": "markdown", + "id": "7e9b8125", + "metadata": {}, + "source": [ + "### Run Ecco\n", + "\n", + "Ecco computes the convective/stratiform partition using radar reflectivity in Cartesian coordinates.\n", + "\n", + "We run Ecco by specifying the parameter file, and the start and end times for the analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d48d1d4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "Program 'Ecco'\n", + "Run-time 2022/08/23 23:24:27.\n", + "\n", + "Copyright (c) 1992 - 2022\n", + "University Corporation for Atmospheric Research (UCAR)\n", + "National Center for Atmospheric Research (NCAR)\n", + "Boulder, Colorado, USA.\n", + "\n", + "Redistribution and use in source and binary forms, with\n", + "or without modification, are permitted provided that the following\n", + "conditions are met:\n", + "\n", + "1) Redistributions of source code must retain the above copyright\n", + "notice, this list of conditions and the following disclaimer.\n", + "\n", + "2) Redistributions in binary form must reproduce the above copyright\n", + "notice, this list of conditions and the following disclaimer in the\n", + "documentation and/or other materials provided with the distribution.\n", + "\n", + "3) Neither the name of UCAR, NCAR nor the names of its contributors, if\n", + "any, may be used to endorse or promote products derived from this\n", + "software without specific prior written permission.\n", + "\n", + "4) If the software is modified to produce derivative works, such modified\n", + "software should be clearly marked, so as not to confuse it with the\n", + "version available from UCAR.\n", + "\n", + "======================================================================\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220000.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_220000.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220500.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_220500.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221000.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_221000.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_221500.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222000.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_222000.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222500.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_222500.mdv.cf.nc\n", + "Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_223000.mdv.cf.nc\n", + "Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_223000.mdv.cf.nc\n" + ] + } + ], + "source": [ + "# Run Ecco using param file\n", + "!/usr/local/lrose/bin/Ecco -params ./params/Ecco.nexrad_mosaic -debug -start \"2021 07 06 22 00 00\" -end \"2021 07 06 22 30 00\"" + ] + }, + { + "cell_type": "markdown", + "id": "a6ade643", + "metadata": {}, + "source": [ + "### Read in Ecco results for a selected time" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "dee213c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "root group (NETCDF4 data model, file format HDF5):\n", + " Conventions: CF-1.6\n", + " history: Converted from NetCDF to MDV, 2022/08/23 23:24:47\n", + " Ncf:history: Data merged from following files:\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc\n", + " /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc\n", + "\n", + " Ncf:comment: \n", + " : Stratfinder used to identify stratiform regions\n", + " source: NEXRAD radars\n", + " title: 3D RADAR MOSAIC\n", + " comment: \n", + " dimensions(sizes): time(1), bounds(2), x0(900), y0(700), x1(139), y1(72), z0(1), z1(32), z2(31), nbytes_mdv_chunk_0000(485)\n", + " variables(dimensions): float64 time(time), float64 start_time(time), float64 stop_time(time), float64 time_bounds(time, bounds), float32 x0(x0), float32 y0(y0), float32 x1(x1), float32 y1(y1), float32 z0(z0), float32 z1(z1), float32 z2(z2), int32 grid_mapping_0(), int32 grid_mapping_1(), int32 mdv_master_header(time), int8 mdv_chunk_0000(time, nbytes_mdv_chunk_0000), int16 DbzTextureComp(time, z0, y0, x0), int16 ConvectivityComp(time, z0, y0, x0), int16 DbzComp(time, z0, y0, x0), int16 FractionActive(time, z0, y0, x0), int16 ConvTops(time, z0, y0, x0), int16 StratTops(time, z0, y0, x0), int16 EchoTops(time, z0, y0, x0), int16 TMP(time, z1, y1, x1), int8 EchoTypeComp(time, z0, y0, x0), int16 DbzConv(time, z2, y0, x0), int16 DbzTexture3D(time, z2, y0, x0), int16 Convectivity3D(time, z2, y0, x0), int16 Dbz3D(time, z2, y0, x0), int8 EchoType3D(time, z2, y0, x0)\n", + " groups: \n", + "fillValue: -128\n", + "ecco3D.shape: (1, 31, 700, 900)\n", + "minLonEcco, maxLonEcco: -104.50500106811523 -95.50500106811523\n", + "minLatEcco, maxLatEcco: 35.4950008392334 42.49499702453613\n", + "minHt, maxHt: 1.25 20.0\n", + "ht: [ 1.25 1.5 1.75 2. 2.25 2.5 2.75 3. 3.5 4. 4.5 5.\n", + " 5.5 6. 6.5 7. 7.5 8. 8.5 9. 10. 11. 12. 13.\n", + " 14. 15. 16. 17. 18. 19. 20. ]\n" + ] + } + ], + "source": [ + "# Read in ecco results for a selected time\n", + "filePathEcco = os.path.join(nexradDataDir, 'mdv/ecco/mosaic/20210706/20210706_221500.mdv.cf.nc')\n", + "dsEcco = nc.Dataset(filePathEcco)\n", + "print(dsEcco)\n", + "\n", + "# create 3D ecco type array with nans for missing vals\n", + "\n", + "eccoField = dsEcco['EchoType3D']\n", + "ecco3D = np.array(eccoField)\n", + "fillValue = eccoField._FillValue\n", + "print(\"fillValue: \", fillValue)\n", + "print(\"ecco3D.shape: \", ecco3D.shape)\n", + "\n", + "# if 4D (i.e. time is dim0) change to 3D\n", + "if (len(ecco3D.shape) == 4):\n", + " ecco3D = ecco3D[0]\n", + "\n", + "# Compute Ecco grid limits\n", + "(nZEcco, nYEcco, nXEcco) = ecco3D.shape\n", + "lon = np.array(dsEcco['x0'])\n", + "lat = np.array(dsEcco['y0'])\n", + "ht = np.array(dsEcco['z2'])\n", + "dLonEcco = lon[1] - lon[0]\n", + "dLatEcco = lat[1] - lat[0]\n", + "minLonEcco = lon[0] - dLonEcco / 2.0\n", + "maxLonEcco = lon[-1] + dLonEcco / 2.0\n", + "minLatEcco = lat[0] - dLatEcco / 2.0\n", + "maxLatEcco = lat[-1] + dLatEcco / 2.0\n", + "minHtEcco = ht[0]\n", + "maxHtEcco = ht[-1]\n", + "print(\"minLonEcco, maxLonEcco: \", minLonEcco, maxLonEcco)\n", + "print(\"minLatEcco, maxLatEcco: \", minLatEcco, maxLatEcco)\n", + "print(\"minHt, maxHt: \", minHtEcco, maxHtEcco)\n", + "print(\"ht: \", ht)\n", + "del lon, lat, ht" + ] + }, + { + "cell_type": "markdown", + "id": "504d0204", + "metadata": {}, + "source": [ + "### Compute the column-max of the Ecco 3-D results " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3851cb4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(700, 900)\n", + "[nan nan nan ... nan nan nan]\n", + "nan\n", + "nan\n" + ] + } + ], + "source": [ + "# Compute column-max echo type\n", + "eccoPlaneMax = np.amax(ecco3D, (0))\n", + "eccoPlaneMax[eccoPlaneMax == fillValue] = np.nan\n", + "print(eccoPlaneMax.shape)\n", + "print(eccoPlaneMax[eccoPlaneMax != np.nan])\n", + "print(np.min(eccoPlaneMax[eccoPlaneMax != np.nan]))\n", + "print(np.max(eccoPlaneMax))" + ] + }, + { + "cell_type": "markdown", + "id": "438b696d", + "metadata": {}, + "source": [ + "### Plot the column max of the Ecco results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ec9f964e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Column max of Echo Type for radar mosaic: 2021/07/06-16:14:20 UTC')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot column-max ecco\n", + "figEcco = plt.figure(figsize=(8, 8), dpi=150)\n", + "axEcco = new_map(figEcco)\n", + "plt.imshow(eccoPlaneMax,\n", + " cmap='rainbow', vmin=12, vmax=40,\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLonEcco, maxLonEcco, minLatEcco, maxLatEcco))\n", + "axEcco.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='black')\n", + "axEcco.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", + "cbarEcco = plt.colorbar(label=\"Echo type\", cax=None, orientation=\"vertical\", shrink=0.6)\n", + "cbarEcco.set_ticks([14,16,18,25,32,34,36,38],\n", + " labels=['StratLowLow', 'StratMid', 'StratHigh', 'Mixed',\n", + " 'ConvElev', 'ConvLow', 'ConvMid', 'ConvDeep'])\n", + "\n", + "plt.title(\"Column max of Echo Type for radar mosaic: \" + startTimeStr)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d809ca0c", + "metadata": {}, + "source": [ + "## Plot vertical sections for Echo Type" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "2a831174", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eccoVertWE.shape: (31, 900)\n", + "eccoVertWEMax.shape: (31, 900)\n", + "eccoVertNS.shape: (31, 700)\n", + "eccoVertNSMax.shape: (31, 700)\n" + ] + } + ], + "source": [ + "# Compute W-E Ecco vertical section\n", + "nYHalfEcco = int(nYEcco/2)\n", + "eccoVertWE = ecco3D[:, nYHalfEcco:(nYHalfEcco+1), :]\n", + "eccoVertWE = eccoVertWE.reshape(eccoVertWE.shape[0], eccoVertWE.shape[2])\n", + "print('eccoVertWE.shape: ', eccoVertWE.shape)\n", + "eccoVertWE[eccoVertWE == fillValue] = np.nan\n", + "eccoVertWEMax = np.amax(ecco3D, axis=1)\n", + "eccoVertWEMax[eccoVertWEMax == fillValue] = np.nan\n", + "print('eccoVertWEMax.shape: ', eccoVertWEMax.shape)\n", + "\n", + "# Compute ecco N-S vertical section\n", + "nXHalfEcco = int(nXEcco/2)\n", + "eccoVertNS = ecco3D[:, :, nXHalfEcco:(nXHalfEcco+1)]\n", + "eccoVertNS = eccoVertNS.reshape(eccoVertNS.shape[0], eccoVertNS.shape[1])\n", + "eccoVertNS[eccoVertNS == fillValue] = np.nan\n", + "print('eccoVertNS.shape: ', eccoVertNS.shape)\n", + "eccoVertNSMax = np.amax(ecco3D, axis=2)\n", + "eccoVertNSMax[eccoVertNSMax == fillValue] = np.nan\n", + "print('eccoVertNSMax.shape: ', eccoVertNSMax.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "69ad655e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Echo type vert slice mid NS: 2021/07/06-16:14:20 UTC')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot mid W-E ecco vertical section\n", + "\n", + "figEccoVert = plt.figure(num=2, figsize=[12, 8], layout='constrained')\n", + "axEv1 = figEccoVert.add_subplot(211, xlim = (minLonEcco, maxLonEcco), ylim = (minHtEcco, maxHtEcco))\n", + "plt.imshow(eccoVertWE,\n", + " vmin=12, vmax=40,\n", + " cmap='rainbow',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLonEcco, maxLonEcco, minHtEcco, maxHtEcco))\n", + "axEv1.set_aspect(0.15)\n", + "axEv1.set_xlabel('Longitude (deg)')\n", + "axEv1.set_ylabel('Height (km)')\n", + "plt.title(\"Echo type vert slice mid W-E: \" + startTimeStr)\n", + "\n", + "cbar = plt.colorbar(label=\"ecco\", cax=None, orientation=\"vertical\", shrink=1.5)\n", + "cbar.set_ticks([14,16,18,25,32,34,36,38], labels=['StratLowLow', 'StratMid', 'StratHigh', 'Mixed', 'ConvElev', 'ConvLow', 'ConvMid', 'ConvDeep'])\n", + "\n", + "# Plot mid N-S ecco vertical section\n", + "\n", + "axEv2 = figEccoVert.add_subplot(212, xlim = (minLatEcco, maxLatEcco), ylim = (minHtEcco, maxHtEcco))\n", + "plt.imshow(eccoVertNS,\n", + " vmin=12, vmax=40,\n", + " cmap='rainbow',\n", + " interpolation = 'bilinear',\n", + " origin = 'lower',\n", + " extent = (minLatEcco, maxLatEcco, minHtEcco, maxHtEcco))\n", + "axEv2.set_aspect(0.15)\n", + "axEv2.set_xlabel('Latitude (deg)')\n", + "axEv2.set_ylabel('Height (km)')\n", + "plt.title(\"Echo type vert slice mid NS: \" + startTimeStr)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "634d51c1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/104/_sources/notebooks/notebook-template.ipynb b/_preview/104/_sources/notebooks/notebook-template.ipynb new file mode 100644 index 00000000..dad9f266 --- /dev/null +++ b/_preview/104/_sources/notebooks/notebook-template.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start here! If you can directly link to an image relevant to your notebook, such as [canonical logos](https://github.com/numpy/numpy/blob/main/doc/source/_static/numpylogo.svg), do so here at the top of your notebook. You can do this with Markdown syntax,\n", + "\n", + "> `![](http://link.com/to/image.png \"image alt text\")`\n", + "\n", + "or edit this cell to see raw HTML `img` demonstration. This is preferred if you need to shrink your embedded image. **Either way be sure to include `alt` text for any embedded images to make your content more accessible.**\n", + "\n", + "\"Project" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project Pythia Notebook Template\n", + "\n", + "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n", + "\n", + "1. This is a numbered list of the specific topics\n", + "1. These should map approximately to your main sections of content\n", + "1. Or each second-level, `##`, header in your notebook\n", + "1. Keep the size and scope of your notebook in check\n", + "1. And be sure to let the reader know up front the important concepts they'll be leaving with" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app) Jupyter Book.\n", + "\n", + "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n", + "\n", + "Label the importance of each concept explicitly as **helpful/necessary**.\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n", + "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", + "| Project management | Helpful | |\n", + "\n", + "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n", + "- **System requirements**:\n", + " - Populate with any system, version, or non-Python software requirements if necessary\n", + " - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n", + " - If no extra requirements, remove the **System requirements** point altogether" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your first content section" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# as well as any and all of your code cells\n", + "print(\"Hello world!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A content subsection\n", + "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# some subsection code\n", + "new = \"helpful information\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Another content subsection\n", + "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your second content section\n", + "Here we can move on to our second objective, and we can demonstrate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Subsection to the second section\n", + "\n", + "#### a quick demonstration\n", + "\n", + "##### of further and further\n", + "\n", + "###### header levels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n", + "\n", + "\\begin{align}\n", + "\\dot{x} & = \\sigma(y-x) \\\\\n", + "\\dot{y} & = \\rho x - y - xz \\\\\n", + "\\dot{z} & = -\\beta z + xy\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Last Section\n", + "\n", + "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Info

\n", + " Your relevant information here!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feel free to copy this around and edit or play around with yourself. Some other `admonitions` you can put in:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Success

\n", + " We got this done after all!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Warning

\n", + " Be careful!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Danger

\n", + " Scary stuff be here.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/content/metadata.html#jupyter-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "Add one final `---` marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.\n", + "\n", + "### What's next?\n", + "Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and references\n", + "Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you're done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:\n", + " - `Kernel > Restart Kernel and Run All Cells...` to confirm that your notebook will cleanly run from start to finish\n", + " - `Kernel > Restart Kernel and Clear All Outputs...` before committing your notebook, our machines will do the heavy lifting\n", + " - Take credit! Provide author contact information if you'd like; if so, consider adding information here at the bottom of your notebook\n", + " - Give credit! Attribute appropriate authorship for referenced code, information, images, etc.\n", + " - Only include what you're legally allowed: **no copyright infringement or plagiarism**\n", + " \n", + "Thank you for your contribution!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "nbdime-conflicts": { + "local_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python 3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ], + "remote_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ] + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/answer_question_pyart_meteoswiss.ipynb b/_preview/104/_sources/notebooks/pyart/answer_question_pyart_meteoswiss.ipynb new file mode 100644 index 00000000..25fdb35d --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/answer_question_pyart_meteoswiss.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercice Sample Solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyart\n", + "pyart.config.load_config('mch_config.py')\n", + "import numpy as np\n", + "import cartopy.crs as ccrs\n", + "import cartopy\n", + "import matplotlib.pyplot as plt\n", + "import glob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Load all radar files in /data/question_pyart_meteoswiss and merge them into one single radar object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files_radar = sorted(glob.glob('./data/question_pyart_meteoswiss/MLA211941205*'))\n", + "for i,f in enumerate(files_radar):\n", + " radar = pyart.io.read_cfradial(f)\n", + " \n", + " if i == 0:\n", + " radar_merged = radar\n", + " else:\n", + " radar_merged = pyart.util.join_radar(radar_merged, \n", + " radar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Perform attenuation correction of ZH, using a constant freezing level height of 2700 m." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute attenuation\n", + "out = pyart.correct.calculate_attenuation_zphi(radar_merged, fzl = 4200,\n", + " phidp_field = 'uncorrected_differential_phase',\n", + " temp_ref = 'fixed_fzl')\n", + "spec_at, pia, cor_z, spec_diff_at, pida, cor_zdr = out\n", + "radar_merged.add_field('corrected_reflectivity', cor_z)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Estimate the QPE with a __[a polynomial Z-R relation](https://arm-doe.github.io/pyart/_modules/pyart/retrieve/qpe.html#est_rain_rate_za)__." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qpe = pyart.retrieve.est_rain_rate_zpoly(radar_merged, refl_field = 'corrected_reflectivity')\n", + "\n", + "radar_merged.add_field('radar_estimated_rain_rate', qpe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Compute a CAPPI of the resulting radar estimate rain rate from 500 to 8000 m above the radar using a vertical resolution of 100 m and a horizontal resolution of 500 m at a x and y distance of up to 100 km to the radar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zmin = 500\n", + "zmax = 8000\n", + "ymin= xmin = -100000\n", + "ymax = xmax = 100000\n", + "lat = float(radar.latitude['data'])\n", + "lon = float(radar.longitude['data'])\n", + "alt = float(radar.altitude['data'])\n", + "# number of grid points in cappi\n", + "cappi_res_h = 500\n", + "cappi_res_v = 100\n", + "ny = int((ymax-ymin)/cappi_res_h)+1\n", + "nx = int((xmax-xmin)/cappi_res_h)+1\n", + "nz = int((zmax-zmin)/cappi_res_v)+1\n", + "\n", + "cappi_qpe = pyart.map.grid_from_radars(radar_merged, grid_shape=(nz, ny, nx),\n", + " grid_limits=((zmin, zmax), (ymin, ymax),\n", + " (xmin, xmax)),\n", + " fields=['radar_estimated_rain_rate'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Using numpy, perform a weighted average of all CAPPI levels using the weights. Finally display the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weighting = np.exp(-0.5* cappi_qpe.z['data'] / 1000)\n", + "\n", + "qpe_ground = weighting[:,None,None]*cappi_qpe.fields['radar_estimated_rain_rate']['data']\n", + "qpe_ground = np.nansum(qpe_ground, axis = 0) / np.sum(weighting)\n", + "\n", + "plt.pcolormesh(cappi_qpe.point_longitude['data'][0], \n", + " cappi_qpe.point_latitude['data'][0],\n", + " qpe_ground, vmax = 15)\n", + "plt.colorbar(label = 'Estimated rain rate at ground [mm/h]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compare this QPE with the operational QPE of MeteoSwiss at the same timestep:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"qpe_op.png\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The agreement near the radar is not too bad, even with such a simple aggregation method. In the south we see some large discrepancies. This is due to the fact that the operational QPE includes many additional steps. In this case, the difference is likely due to the correction for partial beam blocking that is performed by the operational QPE in this mountaineous region south of the radar." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/exercice1_swiss_thunderstorm.ipynb b/_preview/104/_sources/notebooks/pyart/exercice1_swiss_thunderstorm.ipynb new file mode 100644 index 00000000..9f5e5fe0 --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/exercice1_swiss_thunderstorm.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering and retrievals on raw Swiss C-band data\n", + "\n", + "In this exercice we will load raw unfiltered Swiss C-band data during a thunderstorm event and process it to ultimately estimate the precipitation intensities. The following topics will be tackled.\n", + "\n", + "- Ground clutter detection\n", + "- Attenuation correction\n", + "- KDP estimation\n", + "- Hydrometeor classification\n", + "- QPE\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "\n", + "import numpy as np\n", + "import cartopy.crs as ccrs\n", + "import cartopy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyart\n", + "pyart.config.load_config('mch_config.py')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that you can create your own Py-ART configuration file, which defines default field names, default colormaps, limits, and much more. This is the one we use at MeteoSwiss. You can then either load it at startup in your python code or define the environment variable PYART_CONFIG to point to your file in your work environment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading the data\n", + "\n", + "Let's start by loading our radar file which is of the standard CFRadial type. It corresponds to the third sweep of the operational radar scans, which is a PPI at 1° elevation. It contains raw radar data (before pre-processing) at a resolution of 83 m. We then add the temperature obtained from the COSMO NWP model to our radar object (note that this temperature was previously interpolated from the model grid to the radar polar grid). Note that the freezing level is quite high in this example (around 4200 m.)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Open radar file\n", + "file_radar = './data/exercice1_swiss_thunderstorm/MHL2217907250U.003.nc'\n", + "radar = pyart.io.read_cfradial(file_radar)\n", + "\n", + "# Add temperature\n", + "temp = pyart.io.read_cfradial('./data/exercice1_swiss_thunderstorm/20220628073500_savevol_COSMO_LOOKUP_TEMP.nc')\n", + "radar.add_field('temperature', temp.fields['temperature'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ground-clutter and noise removal\n", + "\n", + "Py-ART uses gatefilters which are a kind of mask to filter out problematic measurements. Most processing routines can take a gatefilter as input and will ignore pixels that were filtered out. \n", + "\n", + "Here we create a gate __[filter](https://arm-doe.github.io/pyart/API/generated/pyart.filters.moment_and_texture_based_gate_filter.html)__ based on the radar moments and their texture to filter out noise and ground clutter. Since we are interested in a strong thunderstorm we also extend this filter to remove all measurements with a SNR ratio of less than 10 dB.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gtfilter = pyart.filters.moment_and_texture_based_gate_filter(radar)\n", + "gtfilter.exclude_below('signal_to_noise_ratio', 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare visually the reflectivity before and after filtering. Note that the plot function of Py-ART take a gatefilter as input. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(10,6), sharex= True, sharey=True)\n", + "display = pyart.graph.RadarDisplay(radar)\n", + "display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., ax = ax[0], colorbar_label = 'Raw')\n", + "display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., gatefilter = gtfilter, \n", + " ax = ax[1], colorbar_label = 'Filtered')\n", + "ax[0].set_xlim([-50,50])\n", + "ax[0].set_ylim([-50,50])\n", + "ax[0].set_aspect('equal', 'box')\n", + "ax[1].set_aspect('equal', 'box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here it is clear that most ground clutter (mostly north west and east of the radar), as well as noise have been filtered out." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attenuation correction\n", + "\n", + "We can expect strong attenuation behind a thunderstorm like this. So it is a good idea to try to correct for it. Knowledge of the specific attenuation can also be very insightful." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "out = pyart.correct.calculate_attenuation_zphi(radar, fzl = 4200,\n", + " gatefilter=gtfilter,\n", + " phidp_field = 'uncorrected_differential_phase',\n", + " temp_field = 'temperature',\n", + " temp_ref = 'temperature')\n", + "spec_at, pia, cor_z, spec_diff_at, pida, cor_zdr = out\n", + "radar.add_field('corrected_reflectivity', cor_z)\n", + "radar.add_field('corrected_differential_reflectivity', cor_zdr)\n", + "radar.add_field('specific_attenuation', spec_at)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the Z-PHI method, which uses the relation between differential phase shift and specific attenuation. However it works only in the liquid phase. So you need to provide it either with a fixed freezing level height, a field of freezing level heights or a field of temperature. Here we provide the later.\n", + "\n", + "This method provides us with 5 output variables\n", + "- specific attenuation dB/km\n", + "- path integrated attenuation dB\n", + "- corrected reflectivity dBZ\n", + "- differential specific attenuation dB\n", + "- path integrated differential attenuation dB\n", + "- corrected differential reflectivity (ZDR) dB\n", + "\n", + "We will now plot the specific attenuation as well as the raw and corrected reflectivities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,3, figsize=(16,6), sharex= True, sharey=True)\n", + "display = pyart.graph.RadarDisplay(radar)\n", + "display.plot_ppi('specific_attenuation', 0, vmin=0, vmax=1.5, gatefilter = gtfilter,\n", + " ax = ax[0])\n", + "display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., ax = ax[1], gatefilter = gtfilter,\n", + " colorbar_label = 'ZH with attenuation [dBZ]')\n", + "display.plot_ppi('corrected_reflectivity', 0, vmin=0, vmax=60., gatefilter = gtfilter,\n", + " ax = ax[2], colorbar_label = 'ZH attenuation corrected [dBZ]')\n", + "ax[0].set_xlim([-50,50])\n", + "ax[0].set_ylim([-50,50])\n", + "ax[0].set_aspect('equal', 'box')\n", + "ax[1].set_aspect('equal', 'box')\n", + "ax[2].set_aspect('equal', 'box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can clearly observe a strong specific attenuation within the thunderstorm as well as a significant difference in reflectivity before/after correction behind the thunderstorm to the west." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## KDP estimation\n", + "\n", + "Another very interesting radar variable is the specific differential phase shift KDP. Large KDP indicates the presence of large oblate drops and is linked to very strong precipitation. KDP is also needed for the hydrometeor classification algorithm. However KDP is not measured directly and needs to be estimated numerically from the raw differential phase shift (PHIDP). Py-ART provides three different retrieval methods. We will use the method by __[Maesaka et al. (2012)](https://arm-doe.github.io/pyart/API/generated/pyart.retrieve.kdp_maesaka.html)__ which is fast and robust but assumes KDP to be positive and is therefore limited to rainfall below the melting layer and/or warm clouds. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kdp, _, _ = pyart.retrieve.kdp_maesaka(radar, gatefilter = gtfilter,\n", + " psidp_field = 'uncorrected_differential_phase')\n", + "radar.add_field('specific_differential_phase', kdp)\n", + "\n", + "fig, ax = plt.subplots(1,1, figsize=(6,6))\n", + "display = pyart.graph.RadarDisplay(radar)\n", + "display.plot_ppi('specific_differential_phase', 0, vmin = 0, vmax = 10,\n", + " ax = ax, gatefilter = gtfilter)\n", + "\n", + "ax.set_xlim([-50,50])\n", + "ax.set_ylim([-50,50])\n", + "ax.set_aspect('equal', 'box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A look at the KDP field shows clusters of very large KDP (> 5 °/km) at the center of the thunderstorm." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hydrometeor classification\n", + "\n", + "The hydrometeor classification algorithm in Py-ART by __[Besic et al. (2016)](https://arm-doe.github.io/pyart/API/generated/pyart.retrieve.kdp_maesaka.html)__ uses ZH, ZDR, RHOHV, KDP and the temperature to classify hydrometeors into one of 8 classes:\n", + "- Ice hail, high density Graupel\n", + "- Melting hail\n", + "- Wet snow\n", + "- Vertically oriented ice\n", + "- Rain\n", + "- Rimed particles\n", + "- Light rain\n", + "- Crystals\n", + "- Aggregates\n", + "\n", + "This algorithm requires centroids of polarimetric variables for the different hydrometeor classes. Below we provide it with centroids specifically suited for the radar of Monte Lema. If left empty, the algorithm will use default centroids at the right frequency band (X, C or S).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "centroids = np.array([[13.8231,0.2514,0.0644,0.9861,1380.6],\n", + "[3.0239,0.1971,0.,0.9661,1464.1],\n", + "[4.9447,0.1142,0.,0.9787,-974.7],\n", + "[34.2450,0.5540,0.1459,0.9937,945.3],\n", + "[40.9432,1.0110,0.5141,0.9928,-993.5],\n", + "[3.5202,-0.3498,0.,0.9746,843.2],\n", + "[32.5287,0.9751,0.2640,0.9804,-55.5],\n", + "[52.6547,2.7054,2.5101,0.9765,-1114.6],\n", + "[46.4998,0.1978,0.6431,0.9845,1010.1]])\n", + "\n", + "\n", + "hydro = pyart.retrieve.hydroclass_semisupervised(radar, mass_centers = centroids,\n", + " refl_field = 'corrected_reflectivity',\n", + " zdr_field = 'corrected_differential_reflectivity',\n", + " kdp_field = 'specific_differential_phase',\n", + " rhv_field = 'uncorrected_cross_correlation_ratio',\n", + " temp_field = 'temperature')\n", + "\n", + "radar.add_field('radar_echo_classification', hydro)\n", + "\n", + "fig, ax = plt.subplots(1,1, figsize=(6,6))\n", + "display = pyart.graph.RadarDisplay(radar)\n", + "import matplotlib as mpl\n", + "\n", + "labels = ['NC','AG', 'CR', 'LR', 'RP', 'RN', 'VI', 'WS', 'MH', 'IH/HDG']\n", + "ticks = np.arange(len(labels))\n", + "boundaries = np.arange(-0.5, len(labels) )\n", + "norm = mpl.colors.BoundaryNorm(boundaries, 256)\n", + "\n", + "cax = display.plot_ppi('radar_echo_classification', 0, ax = ax, gatefilter = gtfilter,\n", + " norm = norm, ticks = ticks, ticklabs = labels)\n", + "\n", + "ax.set_xlim([-50,50])\n", + "ax.set_ylim([-50,50])\n", + "ax.set_aspect('equal', 'box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the plotting commands are slightly more complicated due to the categorical colormap.\n", + "\n", + "A look at the hydrometeor classification reveals the presence of wet hail in the center of the thunderstorm surrounded by rain and by light rain. A few isolated pixels (unfiltered ground clutter) are also classified as hail. \n", + "There was indeed intense hail at the ground on that day." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QPE\n", + "\n", + "Py-ART provides several QPE algorithms but the most refined relies on the hydrometeor classification and uses different relations between radar variables and precipitation intensities within the different hydrometeor classes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qpe = pyart.retrieve.est_rain_rate_hydro(radar, refl_field = 'corrected_reflectivity',\n", + " hydro_field = 'radar_echo_classification',\n", + " a_field = 'specific_attenuation',\n", + " thresh=40)\n", + "\n", + "radar.add_field('radar_estimated_rain_rate', qpe)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now plot the precipitation intensity on a Cartopy map and add some spatial features (land borders) using __[RadarMapDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.GridMapDisplay.html)__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lon_bnds = [8.2, 9.5]\n", + "lat_bnds = [45.5, 46.5]\n", + "\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "\n", + "fig = plt.figure(figsize=(8,8))\n", + "display.plot_ppi_map('radar_estimated_rain_rate', 0, vmin=0, vmax=120.,\n", + " colorbar_label='', title='Precipitation intensity [mm/h]', gatefilter = gtfilter,\n", + " min_lon = lon_bnds[0], max_lon = lon_bnds[1],mask_outside = True,\n", + " min_lat = lat_bnds[0], max_lat = lat_bnds[1],\n", + " lon_lines=np.arange(lon_bnds[0], lon_bnds[1], .2), resolution='10m',\n", + " lat_lines=np.arange(lat_bnds[0], lat_bnds[1], .2),\n", + " lat_0=radar.latitude['data'][0],\n", + " lon_0=radar.longitude['data'][0], embellish=True)\n", + "\n", + "states_provinces = cartopy.feature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_0_countries',\n", + " scale='10m',\n", + " facecolor='none')\n", + "lakes = cartopy.feature.NaturalEarthFeature(\n", + " category='physical',\n", + " name='lakes',\n", + " scale='10m',\n", + " facecolor='blue')\n", + "rivers = cartopy.feature.NaturalEarthFeature(\n", + " category='physical',\n", + " name='rivers',\n", + " scale='10m',\n", + " facecolor='blue')\n", + "display.ax.add_feature(states_provinces, edgecolor='gray')\n", + "display.ax.add_feature(lakes, edgecolor='blue', alpha = 0.25)\n", + "display.ax.add_feature(cartopy.feature.RIVERS)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we didn't estimate precipitation intensity at the ground but only aloft. Within the thunderstorm precipitation intensity is extremely high. This is likely too high because QPE in wet hail is very uncertain. However, even the operational QPE algorithm at MeteoSwiss estimated precipitation intensities at the ground close to 120 mm/h. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/exercice2_swiss_doppler.ipynb b/_preview/104/_sources/notebooks/pyart/exercice2_swiss_doppler.ipynb new file mode 100644 index 00000000..4bd7cf2c --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/exercice2_swiss_doppler.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Processing of Doppler wind data from a Swiss volumetric scan\n", + "\n", + "In this exercice we will load low-resolution filtered Swiss C-band data and process it to estimate a profile of horizontal wind aloft the radar. The following topics will be tackled.\n", + "\n", + "- Dealiasing of radial velocity\n", + "- CAPPI plots and profiles\n", + "- PseudoRHI profiles\n", + "- Computation of a VAD (velocity azimuth display)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "\n", + "import numpy as np\n", + "import cartopy.crs as ccrs\n", + "import cartopy\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "\n", + "import pyart\n", + "pyart.config.load_config('mch_config.py')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Reading and dealiasing the data\n", + "\n", + "The Swiss operational C-band radar network performs 20 PPIs at 20 different elevations (from 0.2° to 40°) every 5 minutes. All PPIs are stored in separate files. \n", + "\n", + "We will thus read all 20 elevations one after the other for a given timestep and use the Py-ART function __[join_radar](https://arm-doe.github.io/pyart/API/generated/pyart.util.join_radar.html?highlight=join_radar)__ to merge them all into a single radar object which containes 20 sweeps. \n", + "\n", + "To avoid using too much memory we will read the pre-processed MeteoSwiss radar data which has a resolution of 500 m in range.\n", + "\n", + "At the end, we will also dealias the radial velocity field. Indeed, at lower elevations, low PRFs are used which result in low Nyquist velocities between 8-12 m/s. This means that a lot of folding will occur, especially in strong winds. In this example we use the simplest dealiasing method of Py-ART which performs by finding regions of similar velocities and unfolding and merging pairs of regions until all regions are unfolded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read all 20 elevations for one timestep\n", + "files_radar = sorted(glob.glob('./data/exercice2_swiss_doppler/MLL221790725*'))\n", + "for i,f in enumerate(files_radar):\n", + " radar = pyart.io.read_cfradial(f)\n", + " \n", + " if i == 0:\n", + " radar_merged = radar\n", + " else:\n", + " radar_merged = pyart.util.join_radar(radar_merged, \n", + " radar)\n", + " \n", + "corr_vel = pyart.correct.dealias_region_based(radar_merged)\n", + "radar_merged.add_field('corrected_velocity', corr_vel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now plot the raw and dealiased velocities at two different elevations to see the effect of the correction.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(2,2, figsize=(10,10), sharex= True, sharey=True)\n", + "ax = ax.ravel()\n", + "display = pyart.graph.RadarDisplay(radar_merged)\n", + "display.plot_ppi('velocity', 2, vmin=-30, vmax=30., ax = ax[0], title='El=1 deg',\n", + " colorbar_label = 'Mean Doppler velocity (m/s)')\n", + "display.plot_ppi('corrected_velocity', 2, vmin=-30, vmax=30., title='El=1 deg',\n", + " ax = ax[1], colorbar_label = 'corr. Mean Doppler velocity (m/s)')\n", + "display.plot_ppi('velocity', 6, vmin=-30, vmax=30., ax = ax[2], title='El=4.5 deg',\n", + " colorbar_label = 'Mean Doppler velocity (m/s)')\n", + "display.plot_ppi('corrected_velocity', 6, vmin=-30, vmax=30., title='El=4.5 deg',\n", + " ax = ax[3], colorbar_label = 'corr. Mean Doppler velocity (m/s)')\n", + "ax[0].set_xlim([-50,50])\n", + "ax[0].set_ylim([-50,50])\n", + "for a in ax:\n", + " a.set_aspect('equal', 'box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indeed the raw velocity shows alternating bands of negative and positive velocities which indicates aliasing. The dealiased velocity looks much less discontinuous. Note however that a major difficulty for these algorithms is the presence of isolated pixels, which tend to get arbitrary values as can be seen in the south of the radar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CAPPI plots\n", + "\n", + "We will now create a CAPPI (constant altitude PPI) of the reflectivity during this event. The idea is to interpolate the volumetric scan on a 3D Cartesian grid using the function __[grid_from_radars](https://arm-doe.github.io/pyart/API/generated/pyart.map.grid_from_radars.html)__. Here we will create slices every 500 m from 500 m to 8000 m above the radar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zmin = 500\n", + "zmax = 8000\n", + "ymin= xmin = -100000\n", + "ymax = xmax = 100000\n", + "lat = float(radar.latitude['data'])\n", + "lon = float(radar.longitude['data'])\n", + "alt = float(radar.altitude['data'])\n", + "# number of grid points in cappi\n", + "cappi_res_h = 500\n", + "cappi_res_v = 500\n", + "ny = int((ymax-ymin)/cappi_res_h)+1\n", + "nx = int((xmax-xmin)/cappi_res_h)+1\n", + "nz = int((zmax-zmin)/cappi_res_v)+1\n", + "\n", + "cappi_zh = pyart.map.grid_from_radars(radar_merged, grid_shape=(nz, ny, nx),\n", + " grid_limits=((zmin, zmax), (ymin, ymax),\n", + " (xmin, xmax)),\n", + " fields=['reflectivity'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we plot the reflectivity at 4 different altitudes (0.5, 3, 5.5 and 8 km), as well as a profile along as a W-E profile at the radar location throught the thunderstorm." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display = pyart.graph.GridMapDisplay(cappi_zh)\n", + "projection = ccrs.PlateCarree()\n", + "fig = plt.figure(figsize=(18,14))\n", + "ax = plt.subplot(221, projection = projection)\n", + "display.plot_grid('reflectivity',0, ax = ax, projection = projection)\n", + "ax = plt.subplot(222, projection = projection)\n", + "display.plot_grid('reflectivity',5, ax = ax, projection = projection)\n", + "ax = plt.subplot(223, projection = projection)\n", + "display.plot_grid('reflectivity',10, ax = ax, projection = projection)\n", + "ax = plt.subplot(224, projection = projection)\n", + "display.plot_grid('reflectivity',15, ax = ax, projection = projection)\n", + "\n", + "ax = fig.add_axes([0.25, -0.20, .5, .25])\n", + "display.plot_latitude_slice('reflectivity', lon=lon, lat=lat, ax = ax)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now create a pseudo RHI (altitudinal cross-section through a set of PPIs) of the radial velocity and the reflectivity through the thunderstorm at azimuth 270° (to the west)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pseudorhi = pyart.util.cross_section_ppi(radar_merged, [270])\n", + "\n", + "display = pyart.graph.RadarDisplay(pseudorhi)\n", + "fig, ax = plt.subplots(2,1, sharex=True,sharey=True, figsize= (10,10))\n", + "display.plot_rhi('corrected_velocity', ax = ax[0], vmin = -30, vmax = 30)\n", + "display.plot_rhi('reflectivity', ax = ax[1])\n", + "ax[0].set_ylim([0,20])\n", + "ax[0].set_xlim([0,100])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convention at MeteoSwiss (which is different from the one used by Py-ART) is that positive velocities are moving away from the radar. In this example we see clearly the downdraft in the center of storm and the updraft in its surroundings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Velocity azimuth display (VAD)\n", + "\n", + "We will now make a VAD retrieval to estimate the horizontal wind profile above the radar. This technique requires to have measurements in as many azimuths as possible and works better for stratiform rain when the radar coverage is wider. We will load data from a cold front event on the 13th July 2021 near the Albis radar (south of Zürich), that showed widespread precipitation around the radar. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately the VAD estimation technique in Py-ART can process only one sweep at a time. So we will average the wind vectors obtained over all sweeps to obtain a more reliable estimate. Note that we skip the first four sweeps which are more prone to ground echoes and have a very low Nyquist velocity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files_radar = sorted(glob.glob('./data/question_pyart_meteoswiss/MLA211941205*'))\n", + "u_allsweeps = []\n", + "v_allsweeps = [] \n", + "zlevels = np.arange(100,5000,100)\n", + "speed = []\n", + "for i,f in enumerate(files_radar[4:]):\n", + " radar = pyart.io.read_cfradial(f)\n", + " corr_vel = pyart.correct.dealias_region_based(radar)\n", + " corr_vel['data'] *= -1 \n", + " radar.add_field('corrected_velocity_neg', corr_vel)\n", + " \n", + " vad = pyart.retrieve.vad_browning(radar, 'corrected_velocity_neg', z_want = zlevels)\n", + " u_allsweeps.append(vad.u_wind)\n", + " v_allsweeps.append(vad.v_wind)\n", + " \n", + "u_avg = np.nanmean(np.array(u_allsweeps), axis = 0)\n", + "v_avg = np.nanmean(np.array(v_allsweeps), axis = 0)\n", + "orientation = np.rad2deg(np.arctan2(-u_avg, -v_avg))%360\n", + "speed = np.sqrt(u_avg**2 + v_avg**2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that because the convention at MeteoSwiss is different than the one in Py-ART we have to flip the sign of the radial velocity field. \n", + "\n", + "Finally we do a plot of the vertical profiles or horizontal wind speed and direction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(1,2, sharey=True)\n", + "ax[0].plot(speed*2, zlevels+radar.altitude['data'])\n", + "ax[1].plot(orientation, zlevels+radar.altitude['data'])\n", + "ax[0].set_xlabel('Wind speed [m/s]')\n", + "ax[1].set_xlabel('Wind direction [deg]')\n", + "ax[0].set_ylabel('Altitude [m]')\n", + "fig.suptitle('Wind profile obtained from VAD')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's compare this wind profile with the one recorded by the nearest radiosounding operated in Payerne (around 130 km west from the radar):\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"radiosounding_pay_20210713\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Though there are some discrepancies the match is not bad, given the distance and the very different ways of measuring wind!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/pyart-basics.ipynb b/_preview/104/_sources/notebooks/pyart/pyart-basics.ipynb new file mode 100644 index 00000000..2a4a3c22 --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/pyart-basics.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Py-ART Basics\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + " \n", + "Within this notebook, we will cover:\n", + "\n", + "1. General overview of Py-ART and its functionality\n", + "1. Reading data using Py-ART\n", + "1. An overview of the `pyart.Radar` object\n", + "1. Create a Plot of our Radar Data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Prerequisites\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Basic features |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic plotting |\n", + "| [NumPy Basics](https://foundations.projectpythia.org/core/numpy/numpy-basics.html) | Helpful | Basic arrays |\n", + "\n", + "- **Time to learn**: 45 minutes\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "import pyart\n", + "from pyart.testing import get_test_data\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An Overview of Py-ART" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### History of the Py-ART\n", + "\n", + " * Development began to address the needs of ARM with the acquisition of a number of\n", + " new scanning cloud and precipitation radar as part of the American Recovery Act.\n", + " * The project has since expanded to work with a variety of weather radars and a wider user\n", + " base including radar researchers and climate modelers.\n", + " * The software has been released on GitHub as open source software under a BSD license.\n", + " Runs on Linux, OS X. It also runs on Windows with more limited functionality.\n", + "\n", + "### What can PyART Do?\n", + "\n", + "Py-ART can be used for a variety of tasks from basic plotting to more complex\n", + "processing pipelines. Specific uses for Py-ART include:\n", + "\n", + " * Reading radar data in a variety of file formats.\n", + " * Creating plots and visualization of radar data.\n", + " * Correcting radar moments while in antenna coordinates, such as:\n", + " * Doppler unfolding/de-aliasing.\n", + " * Attenuation correction.\n", + " * Phase processing using a Linear Programming method.\n", + " * Mapping data from one or multiple radars onto a Cartesian grid.\n", + " * Performing retrievals.\n", + " * Writing radial and Cartesian data to NetCDF files." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading in Data Using Py-ART" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading data in using `pyart.io.read`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When reading in a radar file, we use the `pyart.io.read` module.\n", + "\n", + "`pyart.io.read` can read a variety of different radar formats, such as Cf/Radial, LASSEN, and more. \n", + "The documentation on what formats can be read by Py-ART can be found here:\n", + "\n", + "* [Py-ART IO Documentation](https://arm-doe.github.io/pyart/API/generated/pyart.io.html)\n", + "\n", + "For most file formats listed on the page, using `pyart.io.read` should suffice since Py-ART has the ability to automatically detect the file format.\n", + "\n", + "Let's check out what arguments arguments `pyart.io.read()` takes in!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pyart.io.read?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use a sample data file from `pyart` - which is [**cfradial** format](https://github.com/NCAR/CfRadial).\n", + "\n", + "When we read this in, we get a [`pyart.Radar` object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Radar.html#pyart.core.Radar)!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file = get_test_data('swx_20120520_0641.nc')\n", + "radar = pyart.io.read(file)\n", + "radar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Investigate the [`pyart.Radar` object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Radar.html#pyart.core.Radar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Within this [`pyart.Radar` object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Radar.html#pyart.core.Radar) object are the actual data fields.\n", + "\n", + "This is where data such as reflectivity and velocity are stored.\n", + "\n", + "To see what fields are present we can add the fields and keys additions to the variable where the radar object is stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "radar.fields.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract a sample data field" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fields are stored in a dictionary, each containing coordinates, units and more.\n", + "All can be accessed by just adding the fields addition to the radar object variable.\n", + "\n", + "For an individual field, we add a string in brackets after the fields addition to see\n", + "the contents of that field.\n", + "\n", + "Let's take a look at `'corrected_reflectivity_horizontal'`, which is a common field to investigate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(radar.fields['corrected_reflectivity_horizontal'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can go even further in the dictionary and access the actual reflectivity data.\n", + "\n", + "We use add `'data'` at the end, which will extract the **data array** (which is a masked numpy array) from the dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "reflectivity = radar.fields['corrected_reflectivity_horizontal']['data']\n", + "print(type(reflectivity), reflectivity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets' check the size of this array..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reflectivity.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This reflectivity data array, numpy array, is a two-dimensional array with dimensions:\n", + "- Gates (number of samples away from the radar)\n", + "- Rays (direction around the radar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(radar.nrays, radar.ngates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we wanted to look the 300th ray, at the second gate, we would use something like the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(reflectivity[300, 2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting our Radar Data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### An Overview of Py-ART Plotting Utilities\n", + "\n", + "Now that we have loaded the data and inspected it, the next logical thing to do is to visualize the data! Py-ART's visualization functionality is done through the objects in the [pyart.graph](https://arm-doe.github.io/pyart/API/generated/pyart.graph.html) module.\n", + "\n", + "In Py-ART there are 4 primary visualization classes in pyart.graph:\n", + "\n", + "* [RadarDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.RadarDisplay.html)\n", + "* [RadarMapDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.RadarMapDisplay.html)\n", + "* [AirborneRadarDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.AirborneRadarDisplay.html)\n", + "\n", + "Plotting grid data\n", + "* [GridMapDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.GridMapDisplay.html)\n", + "\n", + "### Use the [RadarMapDisplay](https://arm-doe.github.io/pyart/API/generated/pyart.graph.RadarMapDisplay.html) with our data\n", + "\n", + "For the this example, we will be using `RadarMapDisplay`, using Cartopy to deal with geographic coordinates.\n", + "\n", + "\n", + "We start by creating a figure first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[10, 10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have a figure, let's add our `RadarMapDisplay`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[10, 10])\n", + "display = pyart.graph.RadarMapDisplay(radar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding our map display without specifying a field to plot **won't do anything** we need to specifically add a field to field using `.plot_ppi_map()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi_map('corrected_reflectivity_horizontal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, it will plot the elevation scan, the the default colormap from `Matplotlib`... let's customize!\n", + "\n", + "We add the following arguements:\n", + "- `sweep=3` - The fourth elevation scan (since we are using Python indexing)\n", + "- `vmin=-20` - Minimum value for our plotted field/colorbar\n", + "- `vmax=60` - Maximum value for our plotted field/colorbar\n", + "- `projection=ccrs.PlateCarree()` - Cartopy latitude/longitude coordinate system\n", + "- `cmap='pyart_HomeyerRainbow'` - Colormap to use, selecting one provided by PyART " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[12, 12])\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "display.plot_ppi_map('corrected_reflectivity_horizontal',\n", + " sweep=3,\n", + " vmin=-20,\n", + " vmax=60,\n", + " projection=ccrs.PlateCarree(),\n", + " cmap='pyart_HomeyerRainbow')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change many parameters in the graph by changing the arguments to plot_ppi_map. As you can recall from earlier. simply view these arguments in a Jupyter notebook by typing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi_map?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, let's change the colormap to something different" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[12, 12])\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "display.plot_ppi_map('corrected_reflectivity_horizontal',\n", + " sweep=3,\n", + " vmin=-20,\n", + " vmax=60,\n", + " projection=ccrs.PlateCarree(),\n", + " cmap='pyart_Carbone42')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, let's view a different elevation scan! To do this, change the sweep parameter in the plot_ppi_map function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[12, 12])\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "display.plot_ppi_map('corrected_reflectivity_horizontal',\n", + " sweep=0,\n", + " vmin=-20,\n", + " vmax=60,\n", + " projection=ccrs.PlateCarree(),\n", + " cmap='pyart_Carbone42')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at a different field - for example, correlation coefficient (`corr_coeff`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[12, 12])\n", + "display = pyart.graph.RadarMapDisplay(radar)\n", + "display.plot_ppi_map('copol_coeff',\n", + " sweep=0,\n", + " vmin=0.8,\n", + " vmax=1.,\n", + " projection=ccrs.PlateCarree(),\n", + " cmap='pyart_Carbone42')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Summary\n", + "Within this notebook, we covered the basics of working with radar data using `pyart`, including:\n", + "- Reading in a file using `pyart.io`\n", + "- Investigating the `Radar` object\n", + "- Visualizing radar data using the `RadarMapDisplay`\n", + "\n", + "### What's Next\n", + "In the next few notebooks, we walk through gridding radar data, applying data cleaning methods, and advanced visualization methods!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and References\n", + "Py-ART essentials links:\n", + "\n", + "* [Landing page](arm-doe.github.io/pyart/)\n", + "* [Examples](https://arm-doe.github.io/pyart/examples/index.html)\n", + "* [Source Code](github.com/ARM-DOE/pyart)\n", + "* [Mailing list](groups.google.com/group/pyart-users/)\n", + "* [Issue Tracker](github.com/ARM-DOE/pyart/issues)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.9 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/pyart-gridding.ipynb b/_preview/104/_sources/notebooks/pyart/pyart-gridding.ipynb new file mode 100644 index 00000000..6b4f4ff5 --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/pyart-gridding.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Py-ART Gridding \n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + " \n", + "Within this notebook, we will cover:\n", + "\n", + "1. What is gridding and why is it important?\n", + "1. An overview of gridding with Py-ART \n", + "1. How to choose a gridding routine\n", + "1. Gridding multiple radars to the same grid\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Prerequisites\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Py-ART Basics](pyart-basics) | Helpful | Basic features |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Basic features |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic plotting |\n", + "| [NumPy Basics](https://foundations.projectpythia.org/core/numpy/numpy-basics.html) | Helpful | Basic arrays |\n", + "\n", + "- **Time to learn**: 45 minutes\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "import pyart\n", + "from pyart.testing import get_test_data\n", + "\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is gridding and why is it important?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Antenna vs. Cartesian Coordinates\n", + "\n", + "Radar data, by default, is stored in a **polar (or antenna) coordinate system**, with the data coordinates stored as an angle (ranging from 0 to 360 degrees with 0 == North), and a radius from the radar, and an elevation which is the angle between the ground and the ground.\n", + "\n", + "This format can be challenging to plot, since it is scan/radar specific. Also, it can make comparing with model data, which is on a lat/lon grid, challenging since one would need to **transform** the model daa cartesian coordinates to polar/antenna coordiantes.\n", + "\n", + "Fortunately, PyART has a variety of gridding routines, which can be used to **grid your data to a Cartesian grid**. Once it is in this new grid, one can easily slice/dice the dataset, and compare to other data sources.\n", + "\n", + "### Why is Gridding Important?\n", + "\n", + "Gridding is essential to combining multiple data sources (ex. multiple radars), and comparing to other data sources (ex. model data). There are also decisions that are made during the gridding process that have a large impact on the regridded data - for example:\n", + "- What resolution should my grid be?\n", + "- Which interpolation routine should I use?\n", + "- How smooth should my interpolated data be?\n", + "\n", + "While there is not always a right or wrong answer, it is important to understand the options available, and document which routine you used with your data! Also - experiment with different options and choose the best for your use case!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An overview of gridding with Py-ART\n", + "Let's dig into the regridding process with PyART!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read in and Visualize a Test Dataset\n", + "Let's start with the same file used in the previous notebook (`PyART Basics`), which is a radar file from Northern Oklahoma." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file = get_test_data('swx_20120520_0641.nc')\n", + "radar = pyart.io.read(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot up quick look of reflectivity, at the lowest elevation scan (closest to the ground)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[12, 12])\n", + "display = pyart.graph.RadarDisplay(radar)\n", + "display.plot_ppi('corrected_reflectivity_horizontal',\n", + " cmap='pyart_HomeyerRainbow')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned before, the dataset is currently in the **antenna coordinate system** measured as distance from the radar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup our Gridding Routine with `pyart.map.grid_from_radars()`\n", + "\n", + "Py-ART has the [Grid object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Grid.html#pyart.core.Grid) which has characteristics similar to that of the [Radar object](https://arm-doe.github.io/pyart/API/generated/pyart.core.Radar.html), except that the data are stored in Cartesian coordinates instead of the radar's antenna coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pyart.core.Grid?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can **transform our data** into this grid object, from the radars, using `pyart.map.grid_from_radars()`.\n", + "\n", + "Beforing gridding our data, we need to make a decision about the desired grid resolution and extent. For example, one might imagine a grid configuration of:\n", + "- Grid extent/limits\n", + " - 20 km in the x-direction (north/south)\n", + " - 20 km in the y-direction (west/east)\n", + " - 15 km in the z-direction (vertical)\n", + "- 500 m spatial resolution\n", + "\n", + "The `pyart.map.grid_from_radars()` function takes the grid shape and grid limits as input, with the order `(z, y, x)`.\n", + "\n", + "Let's setup our configuration, setting our grid extent **first**, with the distance measured in **meters**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z_grid_limits = (500.,15_000.)\n", + "y_grid_limits = (-20_000.,20_000.)\n", + "x_grid_limits = (-20_000.,20_000.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our grid limits, we can set our desired resolution (again, in meters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid_resolution = 500" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compute our grid shape - using the extent and resolution to compute the number of grid points in each direction." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_number_of_points(extent, resolution):\n", + " return int((extent[1] - extent[0])/resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a helper function to compute this, let's apply it to our vertical dimension" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z_grid_points = compute_number_of_points(z_grid_limits, grid_resolution)\n", + "z_grid_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply this to the horizontal (x, y) dimensions as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_grid_points = compute_number_of_points(x_grid_limits, grid_resolution)\n", + "y_grid_points = compute_number_of_points(y_grid_limits, grid_resolution)\n", + "\n", + "print(z_grid_points,\n", + " y_grid_points,\n", + " x_grid_points)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use our configuration to grid the data!\n", + "Now that we have the grid shape and grid limits, let's grid up our radar!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grid = pyart.map.grid_from_radars(radar,\n", + " grid_shape=(z_grid_points,\n", + " y_grid_points,\n", + " x_grid_points),\n", + " grid_limits=(z_grid_limits,\n", + " y_grid_limits,\n", + " x_grid_limits),\n", + " )\n", + "grid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a `pyart.core.Grid` object!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot up the Grid Object\n", + "\n", + "#### Plot a horizontal view of the data\n", + "We can use the `GridMapDisplay` from `pyart.graph` to visualize our regridded data, starting with a horizontal view (slice along a single vertical level)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display = pyart.graph.GridMapDisplay(grid)\n", + "display.plot_grid('corrected_reflectivity_horizontal',\n", + " level=0,\n", + " vmin=-20,\n", + " vmax=60,\n", + " cmap='pyart_HomeyerRainbow')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot a Latitudinal Slice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also slice through a single latitude or longitude!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_latitude_slice('corrected_reflectivity_horizontal',\n", + " lat=36.5,\n", + " vmin=-20,\n", + " vmax=60,\n", + " cmap='pyart_HomeyerRainbow')\n", + "plt.xlim([-20, 20]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot with Xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another neat feature of the `Grid` object is that we can transform it to an `xarray.Dataset`!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = grid.to_xarray()\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, our plotting routine is a **one-liner**, starting with the horizontal slice:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.isel(z=0).corrected_reflectivity_horizontal.plot(cmap='pyart_HomeyerRainbow',\n", + " vmin=-20,\n", + " vmax=60);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And a vertical slice at a given y dimension (latitude)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(y=1300,\n", + " method='nearest').corrected_reflectivity_horizontal.plot(cmap='pyart_HomeyerRainbow',\n", + " vmin=-20,\n", + " vmax=60);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Summary\n", + "Within this notebook, we covered the basics of gridding radar data using `pyart`, including:\n", + "- What we mean by gridding and why is it matters\n", + "- Configuring your gridding routine\n", + "- Visualize gridded radar data\n", + "\n", + "### What's Next\n", + "In the next few notebooks, we walk through applying data cleaning methods, and advanced visualization methods!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and References\n", + "Py-ART essentials links:\n", + "\n", + "* [Landing page](arm-doe.github.io/pyart/)\n", + "* [Examples](https://arm-doe.github.io/pyart/examples/index.html)\n", + "* [Source Code](github.com/ARM-DOE/pyart)\n", + "* [Mailing list](groups.google.com/group/pyart-users/)\n", + "* [Issue Tracker](github.com/ARM-DOE/pyart/issues)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart/question_pyart_meteoswiss.ipynb b/_preview/104/_sources/notebooks/pyart/question_pyart_meteoswiss.ipynb new file mode 100644 index 00000000..3c2943d4 --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart/question_pyart_meteoswiss.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercice\n", + "\n", + "The goal of this exercice is to do a QPE for every sweep and perform a weighted average of precipitation aloft to get an estimation of the precipitation intensity at the ground. The following steps will need to be taken:\n", + "\n", + "1. Load all radar files in */data/question_pyart_meteoswiss* and merge them into one single radar object\n", + "2. Perform attenuation correction of ZH, you can use a constant freezing level height of 2700 m.\n", + "3. Estimate the QPE with a __[a polynomial Z-R relation](https://arm-doe.github.io/pyart/_modules/pyart/retrieve/qpe.html#est_rain_rate_za)__.\n", + "4. Compute a CAPPI of the resulting radar estimate rain rate from 500 to 8000 m above the radar using a vertical resolution of 100 m and a horizontal resolution of 500 m.\n", + "5. Using numpy, perform a weighted average of all CAPPI levels using the weights \n", + "\n", + "
$w(z) = exp(-0.5\\cdot z)$, where $z$ is the height above the radar in km.
\n", + "\n", + "Finally display the resulting QPE on its latitude/longitude grid using *pcolormesh* (matplotlib)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The solution can be found [here](answer_question_pyart_meteoswiss.ipynb)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.ipynb b/_preview/104/_sources/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.ipynb new file mode 100644 index 00000000..0d7811ff --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART.\n", + "## This is a severe flooding case from July 8, 2013 in Toronto, Canada, with radar data from the King City, Ontario, radar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the necessary modules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pyart\n", + "import baltrad_pyart_bridge as bridge # routines to pass data from Py-ART and BALTRAD\n", + "import _raveio # BALTRAD's input/output module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in the data using RAVE (a component of BALTRAD)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rain rate retrieval using specific attenuation using BALTRAD and Py-ART" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio = _raveio.open('data/WKR_201307082030.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the data to a Py-ART Radar object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "radar = bridge.raveio2radar(rio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine some of the radar moments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display = pyart.graph.RadarDisplay(radar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('DBZH', 0, vmin=-15, vmax=60)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('PHIDP', 0, vmin=0, vmax=180)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('RHOHV', 0, vmin=0, vmax=1.0, mask_outside=False)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('SQIH', 0, vmin=0, vmax=1, mask_outside=False)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the specific attenuation and attenuation corrected reflectivity using Py-ART, add these field to the radar object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spec_at, cor_z = pyart.correct.calculate_attenuation(\n", + " radar, 0, doc=0, refl_field='DBZH', ncp_field='SQIH', \n", + " rhv_field='RHOHV', phidp_field='PHIDP', \n", + " fzl=8000,)\n", + "# use the parameter below for a more 'cleanup up' attenuation field\n", + "#ncp_min=-1, rhv_min=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "radar.add_field('specific_attenuation', spec_at)\n", + "radar.add_field('corrected_reflectivity', cor_z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine these two new fields." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('specific_attenuation', 0, vmin=0, vmax=0.1)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('corrected_reflectivity', 0, vmin=-15, vmax=60)\n", + "display.plot_range_rings([50, 100, 150])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the rain rate from the specific attenuation using a power law determined from the ARM Southern Great Plains site. Mask values where the attenuation is not valid (when the cross correlation ratio or signal quality is low). Add this field to the radar object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "R = 300.0 * (radar.fields['specific_attenuation']['data']) ** 0.89\n", + "rain_rate_dic = pyart.config.get_metadata('rain_rate')\n", + "rain_rate_dic['units'] = 'mm/hr'\n", + "rate_not_valid = np.logical_or(\n", + " (radar.fields['SQIH']['data'] < 0.4),\n", + " (radar.fields['RHOHV']['data'] < 0.8))\n", + "rain_rate_dic['data'] = np.ma.masked_where(rate_not_valid, R)\n", + "# fill the missing values with 0 for a nicer plot\n", + "rain_rate_dic['data'] = np.ma.filled(rain_rate_dic['data'], 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "radar.add_field('RATE', rain_rate_dic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine the rain rate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display.plot_ppi('RATE', 0, vmin=0, vmax=50.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new RaveIO object from the Py-ART radar object and write this out using Rave" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rio_out = bridge.radar2raveio(radar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "container = _raveio.new()\n", + "container.object = rio_out.object\n", + "container.save(\"data/WKR_201307082030_with_rain_rate.h5\")\n", + "\n", + "import os\n", + "print(\"ODIM_H5 file is %i bytes large\" % os.path.getsize(\"data/WKR_201307082030_with_rain_rate.h5\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Publish a time series of Cartesian products of corrected reflectivity to BALTRAD's GoogleMapsPlugin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using your Browser, preferably anything except Microsoft Internet Explorer, view a pre-loaded product: http://localhost:8080 Use the small Calendar icon in the control panel to select 2013-07-08 20:30. The dropdown box under the date/time field should read \"King City, ON\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fire up the RAVE Product Generation Framework's server. This is normally done on the command line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.system(\"rave_pgf start\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to this XML-RPC server and feed it file strings of pre-generated products" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob, xmlrpc.client\n", + "\n", + "ipath = \"/home/vagrant/pyart2baltrad/data/cawkr\"\n", + "opath = \"/home/vagrant/miniconda/envs/openradar/rave_gmap/web/data/cawkr_gmaps\"\n", + "\n", + "server = xmlrpc.client.ServerProxy(\"http://localhost:8085/RAVE\")\n", + "\n", + "fstrs = glob.glob(ipath + \"/*.h5\")\n", + "\n", + "for ifstr in fstrs:\n", + " # Output file name must only be date/time string with format: YYYYMMDDHHmm.png\n", + " dt = os.path.split(ifstr)[1].split('_')[2].split('.')[0]\n", + " \n", + " ofstr = opath + \"/%s/%s/%s/%s.png\" % (dt[:4], dt[4:6], dt[6:8], dt)\n", + " response = server.generate(\"se.smhi.rave.creategmapimage\", [ifstr], [\"outfile\",ofstr])\n", + "\n", + "print(\"Generated %i PNG images for Google Maps\" % len(fstrs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Go back to your browser and load a sequence of images. Stop the PGF server." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.system(\"rave_pgf stop\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.ipynb b/_preview/104/_sources/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.ipynb new file mode 100644 index 00000000..338fb820 --- /dev/null +++ b/_preview/104/_sources/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bd16d32b", + "metadata": {}, + "source": [ + "# Doppler Velocity Dealiasing with Py-ART and BALTRAD" + ] + }, + { + "cell_type": "markdown", + "id": "dc068679", + "metadata": {}, + "source": [ + "In this notebook Doppler Velocity data from the ARM C-band SAPR radar is read using Py-ART and dealiased using BALTRAD." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d82c619a", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "4eca08aa", + "metadata": {}, + "source": [ + "Import the necessary modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbbe57b8", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import pyart\n", + "import baltrad_pyart_bridge as bridge # routines to pass data from Py-ART to BALTRAD\n", + "import _dealias # BALTRAD's dealiasing module" + ] + }, + { + "cell_type": "markdown", + "id": "c24af2b3", + "metadata": {}, + "source": [ + "Read in the data using Py-ART" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53cf768f", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "radar = pyart.io.read('data/sgpcsaprppi_20110520095101.nc')" + ] + }, + { + "cell_type": "markdown", + "id": "5d2fb0f8", + "metadata": {}, + "source": [ + "Examine the velocity data using Py-ART Display object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe95f4fc", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "display = pyart.graph.RadarDisplay(radar)\n", + "nyquist_velocity = radar.instrument_parameters['nyquist_velocity']['data'][0]\n", + "display.plot_ppi('velocity', 1, colorbar_label='m/s', \n", + " vmin=-nyquist_velocity, vmax=nyquist_velocity)" + ] + }, + { + "cell_type": "markdown", + "id": "e84e72ce", + "metadata": {}, + "source": [ + "Convert the radar data into a RaveIO object with the velocity data having the correct name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "369e5a3c", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "vel_data = radar.fields['velocity']['data']\n", + "radar.add_field_like('velocity', 'VRAD', vel_data)\n", + "rio = bridge.radar2raveio(radar)" + ] + }, + { + "cell_type": "markdown", + "id": "f17da762", + "metadata": {}, + "source": [ + "Perform Doppler velocity dealiasing using BALTRAD." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9a06da4", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "ret = _dealias.dealias(rio.object)\n", + "print(\"This first scan is dealiased:\"), _dealias.dealiased(rio.object.getScan(0))" + ] + }, + { + "cell_type": "markdown", + "id": "4aba4854", + "metadata": {}, + "source": [ + "Add the dealiased velocity field to the origin Py-ART radar object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8db0929f", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp = bridge.raveio2radar(rio)\n", + "if 'dealiased_velocity' in radar.fields:\n", + " radar.fields.pop('dealiased_velocity')\n", + "radar.add_field_like('velocity', 'dealiased_velocity', temp.fields['VRAD']['data'])" + ] + }, + { + "cell_type": "markdown", + "id": "10c736d4", + "metadata": {}, + "source": [ + "Plot the dealiased velocities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec7b5464", + "metadata": { + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "display.plot_ppi('dealiased_velocity', 1, colorbar_label='m/s', \n", + " vmin=-2*nyquist_velocity, vmax=2*nyquist_velocity)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/104/_sources/notebooks/wradlib/wradlib_data_quality.ipynb b/_preview/104/_sources/notebooks/wradlib/wradlib_data_quality.ipynb new file mode 100644 index 00000000..b002c515 --- /dev/null +++ b/_preview/104/_sources/notebooks/wradlib/wradlib_data_quality.ipynb @@ -0,0 +1,837 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"wradlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# wradlib data quality" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Within this notebook, we will cover:\n", + "\n", + "1. Reading radar volume data into xarray based RadarVolume\n", + "1. Wrapping numpy-based functions to work with Xarray\n", + "1. Clutter detection\n", + "1. Beam Blockage calculation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Xarray Basics](https://tutorial.xarray.dev/intro.html) | Helpful | Basic Dataset/DataArray |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic Plotting |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Projections |\n", + "\n", + "- **Time to learn**: 10 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "import wradlib as wrl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Swiss Radar Data from CfRadial1 Volumes\n", + "\n", + "We use some of the pyrad example data here. Sweeps are provided as single files, so we open each file separately and create the RadarVolume from the open Datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fglob = \"../pyart/data/example_pyrad/22179/MLL22179/MLL2217907250U*.nc\"\n", + "flist = glob.glob(fglob)\n", + "flist.sort()\n", + "print(\"Files available: {}\".format(len(flist)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = [xr.open_dataset(f, group=\"sweep_1\", engine=\"cfradial1\", chunks={}) for f in flist]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = wrl.io.RadarVolume(engine=\"cfradial1\")\n", + "vol.extend(ds)\n", + "vol.sort(key=lambda x: x.time.min())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol.root" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sweep_number = 3\n", + "display(vol[sweep_number])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Clutter detection with Gabella\n", + "\n", + "While in Switzerland, why not use the well-known clutter detection scheme by Marco Gabella et. al.\n", + "\n", + "### Wrap Gabella Clutter detection in Xarray `apply_ufunc`\n", + "\n", + "The routine is implemented in wradlib in pure `Numpy`. `Numpy` based processing routines can be transformed to a first class `Xarray` citizen with the help of `xr.apply_ufunc`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_clutter(da, wsize=5, thrsnorain=0, tr1=6.0, n_p=6, tr2=1.3, rm_nans=False):\n", + " return xr.apply_ufunc(\n", + " wrl.clutter.filter_gabella,\n", + " da,\n", + " input_core_dims=[[\"azimuth\", \"range\"]],\n", + " output_core_dims=[[\"azimuth\", \"range\"]],\n", + " dask=\"parallelized\",\n", + " kwargs=dict(\n", + " wsize=wsize,\n", + " thrsnorain=thrsnorain,\n", + " tr1=tr1,\n", + " n_p=n_p,\n", + " tr2=tr2,\n", + " rm_nans=rm_nans,\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate clutter map\n", + "\n", + "Now we apply Gabella scheme and add the result to the Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swp = vol[sweep_number]\n", + "clmap = swp.reflectivity_hh_clut.pipe(\n", + " extract_clutter, wsize=5, thrsnorain=0.0, tr1=21.0, n_p=23, tr2=1.3, rm_nans=False\n", + ")\n", + "swp = swp.assign({\"CMAP\": clmap})\n", + "display(swp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Reflectivities, Clutter and Cluttermap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from osgeo import osr\n", + "\n", + "wgs84 = osr.SpatialReference()\n", + "wgs84.ImportFromEPSG(4326)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(15, 12))\n", + "\n", + "swpx = swp.sel(range=slice(0, 100000)).pipe(wrl.georef.georeference_dataset, proj=wgs84)\n", + "\n", + "ax1 = fig.add_subplot(221)\n", + "swpx.reflectivity_hh_clut.plot(x=\"x\", y=\"y\", ax=ax1, vmin=0, vmax=60)\n", + "ax1.set_title(\"Reflectivity raw\")\n", + "\n", + "ax2 = fig.add_subplot(222)\n", + "swpx.CMAP.plot(x=\"x\", y=\"y\", ax=ax2)\n", + "ax2.set_title(\"Cluttermap\")\n", + "\n", + "ax3 = fig.add_subplot(223)\n", + "swpx.reflectivity_hh_clut.where(swpx.CMAP == 1).plot(\n", + " x=\"x\", y=\"y\", ax=ax3, vmin=0, vmax=60\n", + ")\n", + "ax3.set_title(\"Clutter\")\n", + "\n", + "ax4 = fig.add_subplot(224)\n", + "swpx.reflectivity_hh_clut.where(swpx.CMAP < 1).plot(\n", + " x=\"x\", y=\"y\", ax=ax4, vmin=0, vmax=60\n", + ")\n", + "ax4.set_title(\"Reflectivity clutter removed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SRTM based clutter and beamblockage processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download needed SRTM data\n", + "\n", + "For the course we already provide the needed SRTM tiles. For normal operation you would need a NASA EARTHDATA account and a connected bearer token.\n", + "\n", + "The data will be loaded using GDAL machinery and transformed into an Xarray DataArray." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "extent = wrl.zonalstats.get_bbox(swpx.x.values, swpx.y.values)\n", + "extent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# apply fake token, data is already available\n", + "os.environ[\"WRADLIB_EARTHDATA_BEARER_TOKEN\"] = \"\"\n", + "# set location of wradlib-data, where wradlib will search for any available data\n", + "os.environ[\"WRADLIB_DATA\"] = \"data/wradlib-data\"\n", + "# get the tiles\n", + "dem = wrl.io.get_srtm(extent.values())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "elevation = wrl.georef.read_gdal_values(dem)\n", + "coords = wrl.georef.read_gdal_coordinates(dem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "elev = xr.DataArray(\n", + " data=elevation,\n", + " dims=[\"y\", \"x\"],\n", + " coords={\"lat\": ([\"y\", \"x\"], coords[..., 1]), \"lon\": ([\"y\", \"x\"], coords[..., 0])},\n", + ")\n", + "elev" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Clutter on DEM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(13, 10))\n", + "ax1 = fig.add_subplot(111)\n", + "\n", + "swpx.CMAP.where(swpx.CMAP == 1).plot(\n", + " x=\"x\", y=\"y\", ax=ax1, vmin=0, vmax=1, cmap=\"turbo\", add_colorbar=False\n", + ")\n", + "ax1.set_title(\"Reflectivity corr\")\n", + "\n", + "ax1.plot(swpx.longitude.values, swpx.latitude.values, marker=\"*\", c=\"r\")\n", + "\n", + "elev.plot(x=\"lon\", y=\"lat\", ax=ax1, zorder=-2, cmap=\"terrain\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use hvplot for interactive zooming and panning\n", + "\n", + "Often it is desirable to quickly zoom and pan in the plots. Although matplotlib has that ability, it still is quite slow. Here `hvplot`, a `holoviews` based plotting framework, can be utilized. As frontend `bokeh` is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import hvplot\n", + "import hvplot.xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to rechunk the coordinates as hvplot needs chunked variables and coords.\n", + "\n", + "todo # vergleichbar machen mit beam blockage angle/entfernung" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cl = (\n", + " swpx.CMAP.where(swpx.CMAP == 1)\n", + " .chunk(chunks={})\n", + " .hvplot.quadmesh(\n", + " x=\"x\", y=\"y\", cmap=\"Reds\", width=800, height=700, clim=(0, 1), alpha=0.6\n", + " )\n", + ")\n", + "dm = elev.hvplot.quadmesh(\n", + " x=\"lon\",\n", + " y=\"lat\",\n", + " cmap=\"terrain\",\n", + " width=800,\n", + " height=700,\n", + " clim=(0, 4000),\n", + " rasterize=True,\n", + ")\n", + "dm * cl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert DEM to spherical coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sitecoords = (swpx.longitude.values, swpx.latitude.values, swpx.altitude.values)\n", + "r = swpx.range.values\n", + "az = swpx.azimuth.values\n", + "bw = 0.8\n", + "beamradius = wrl.util.half_power_radius(r, bw)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rastervalues, rastercoords, proj = wrl.georef.extract_raster_dataset(\n", + " dem, nodata=-32768.0\n", + ")\n", + "\n", + "rlimits = (extent[\"left\"], extent[\"bottom\"], extent[\"right\"], extent[\"top\"])\n", + "# Clip the region inside our bounding box\n", + "ind = wrl.util.find_bbox_indices(rastercoords, rlimits)\n", + "rastercoords = rastercoords[ind[1] : ind[3], ind[0] : ind[2], ...]\n", + "rastervalues = rastervalues[ind[1] : ind[3], ind[0] : ind[2]]\n", + "\n", + "polcoords = np.dstack([swpx.x.values, swpx.y.values])\n", + "# Map rastervalues to polar grid points\n", + "polarvalues = wrl.ipol.cart_to_irregular_spline(\n", + " rastercoords, rastervalues, polcoords, order=3, prefilter=False\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Partial and Cumulative Beamblockage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PBB = wrl.qual.beam_block_frac(polarvalues, swpx.z.values, beamradius)\n", + "PBB = np.ma.masked_invalid(PBB)\n", + "print(PBB.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "CBB = wrl.qual.cum_beam_block_frac(PBB)\n", + "print(CBB.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting Beamblockage\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# just a little helper function to style x and y axes of our maps\n", + "def annotate_map(ax, cm=None, title=\"\"):\n", + " xticks = ax.get_xticks()\n", + " ticks = (xticks / 1000).astype(int)\n", + " ax.set_xticks(xticks)\n", + " ax.set_xticklabels(ticks)\n", + " yticks = ax.get_yticks()\n", + " ticks = (yticks / 1000).astype(int)\n", + " ax.set_yticks(yticks)\n", + " ax.set_yticklabels(ticks)\n", + " ax.set_xlabel(\"Kilometers\")\n", + " ax.set_ylabel(\"Kilometers\")\n", + " if not cm is None:\n", + " plt.colorbar(cm, ax=ax)\n", + " if not title == \"\":\n", + " ax.set_title(title)\n", + " ax.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alt = swpx.z.values\n", + "fig = plt.figure(figsize=(15, 12))\n", + "\n", + "# create subplots\n", + "ax1 = plt.subplot2grid((2, 2), (0, 0))\n", + "ax2 = plt.subplot2grid((2, 2), (0, 1))\n", + "ax3 = plt.subplot2grid((2, 2), (1, 0), colspan=2, rowspan=1)\n", + "\n", + "# azimuth angle\n", + "angle = 270\n", + "\n", + "# Plot terrain (on ax1)\n", + "ax1, dem = wrl.vis.plot_ppi(\n", + " polarvalues, ax=ax1, r=r, az=az, cmap=mpl.cm.terrain, vmin=0.0\n", + ")\n", + "ax1.plot(\n", + " [0, np.sin(np.radians(angle)) * 1e5], [0, np.cos(np.radians(angle)) * 1e5], \"r-\"\n", + ")\n", + "ax1.plot(sitecoords[0], sitecoords[1], \"ro\")\n", + "annotate_map(ax1, dem, \"Terrain within {0} km range\".format(np.max(r / 1000.0) + 0.1))\n", + "ax1.set_xlim(-100000, 100000)\n", + "ax1.set_ylim(-100000, 100000)\n", + "\n", + "# Plot CBB (on ax2)\n", + "ax2, cbb = wrl.vis.plot_ppi(CBB, ax=ax2, r=r, az=az, cmap=mpl.cm.PuRd, vmin=0, vmax=1)\n", + "annotate_map(ax2, cbb, \"Beam-Blockage Fraction\")\n", + "ax2.set_xlim(-100000, 100000)\n", + "ax2.set_ylim(-100000, 100000)\n", + "\n", + "# Plot single ray terrain profile on ax3\n", + "(bc,) = ax3.plot(r / 1000.0, alt[angle, :], \"-b\", linewidth=3, label=\"Beam Center\")\n", + "(b3db,) = ax3.plot(\n", + " r / 1000.0,\n", + " (alt[angle, :] + beamradius),\n", + " \":b\",\n", + " linewidth=1.5,\n", + " label=\"3 dB Beam width\",\n", + ")\n", + "ax3.plot(r / 1000.0, (alt[angle, :] - beamradius), \":b\")\n", + "ax3.fill_between(r / 1000.0, 0.0, polarvalues[angle, :], color=\"0.75\")\n", + "ax3.set_xlim(0.0, np.max(r / 1000.0) + 0.1)\n", + "ax3.set_ylim(0.0, 3000)\n", + "ax3.set_xlabel(\"Range (km)\")\n", + "ax3.set_ylabel(\"Altitude (m)\")\n", + "ax3.grid()\n", + "\n", + "axb = ax3.twinx()\n", + "(bbf,) = axb.plot(r / 1000.0, CBB[angle, :], \"-g\", label=\"BBF\")\n", + "axb.spines[\"right\"].set_color(\"g\")\n", + "axb.tick_params(axis=\"y\", colors=\"g\")\n", + "axb.set_ylabel(\"Beam-blockage fraction\", c=\"g\")\n", + "axb.set_ylim(0.0, 1.0)\n", + "axb.set_xlim(0.0, np.max(r / 1000.0) + 0.1)\n", + "\n", + "\n", + "legend = ax3.legend(\n", + " (bc, b3db, bbf),\n", + " (\"Beam Center\", \"3 dB Beam width\", \"BBF\"),\n", + " loc=\"upper left\",\n", + " fontsize=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting Beamblockage on Curvelinear Grid\n", + "\n", + "Here you get an better impression of the actual beam progression ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def height_formatter(x, pos):\n", + " x = (x - 6370000) / 1000\n", + " fmt_str = \"{:g}\".format(x)\n", + " return fmt_str\n", + "\n", + "\n", + "def range_formatter(x, pos):\n", + " x = x / 1000.0\n", + " fmt_str = \"{:g}\".format(x)\n", + " return fmt_str" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(15, 8))\n", + "\n", + "cgax, caax, paax = wrl.vis.create_cg(fig=fig, rot=0, scale=1)\n", + "\n", + "# azimuth angle\n", + "angle = 270\n", + "\n", + "# fix grid_helper\n", + "er = 6370000\n", + "gh = cgax.get_grid_helper()\n", + "gh.grid_finder.grid_locator2._nbins = 80\n", + "gh.grid_finder.grid_locator2._steps = [1, 2, 4, 5, 10]\n", + "\n", + "# calculate beam_height and arc_distance for ke=1\n", + "# means line of sight\n", + "bhe = wrl.georef.bin_altitude(r, 0, sitecoords[2], re=er, ke=1.0)\n", + "ade = wrl.georef.bin_distance(r, 0, sitecoords[2], re=er, ke=1.0)\n", + "nn0 = np.zeros_like(r)\n", + "# for nice plotting we assume earth_radius = 6370000 m\n", + "ecp = nn0 + er\n", + "# theta (arc_distance sector angle)\n", + "thetap = -np.degrees(ade / er) + 90.0\n", + "\n", + "# zero degree elevation with standard refraction\n", + "bh0 = wrl.georef.bin_altitude(r, 0, sitecoords[2], re=er)\n", + "\n", + "# plot (ecp is earth surface normal null)\n", + "(bes,) = paax.plot(thetap, ecp, \"-k\", linewidth=3, label=\"Earth Surface NN\")\n", + "(bc,) = paax.plot(thetap, ecp + alt[angle, :], \"-b\", linewidth=3, label=\"Beam Center\")\n", + "(bc0r,) = paax.plot(thetap, ecp + bh0, \"-g\", label=\"0 deg Refraction\")\n", + "(bc0n,) = paax.plot(thetap, ecp + bhe, \"-r\", label=\"0 deg line of sight\")\n", + "(b3db,) = paax.plot(\n", + " thetap, ecp + alt[angle, :] + beamradius, \":b\", label=\"+3 dB Beam width\"\n", + ")\n", + "paax.plot(thetap, ecp + alt[angle, :] - beamradius, \":b\", label=\"-3 dB Beam width\")\n", + "\n", + "# orography\n", + "paax.fill_between(thetap, ecp, ecp + polarvalues[angle, :], color=\"0.75\")\n", + "\n", + "# shape axes\n", + "cgax.set_xlim(0, np.max(ade))\n", + "cgax.set_ylim([ecp.min() - 1000, ecp.max() + 2500])\n", + "caax.grid(True, axis=\"x\")\n", + "cgax.grid(True, axis=\"y\")\n", + "cgax.axis[\"top\"].toggle(all=False)\n", + "caax.yaxis.set_major_locator(\n", + " mpl.ticker.MaxNLocator(steps=[1, 2, 4, 5, 10], nbins=20, prune=\"both\")\n", + ")\n", + "caax.xaxis.set_major_locator(mpl.ticker.MaxNLocator())\n", + "caax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(height_formatter))\n", + "caax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(range_formatter))\n", + "\n", + "caax.set_xlabel(\"Range (km)\")\n", + "caax.set_ylabel(\"Altitude (km)\")\n", + "\n", + "legend = paax.legend(\n", + " (bes, bc0n, bc0r, bc, b3db),\n", + " (\n", + " \"Earth Surface NN\",\n", + " \"0 deg line of sight\",\n", + " \"0 deg std refraction\",\n", + " \"Beam Center\",\n", + " \"3 dB Beam width\",\n", + " ),\n", + " loc=\"lower left\",\n", + " fontsize=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Clutter and Beamblockage as Quality Index\n", + "\n", + "Simple masking with cumulative beam blockage and Gabella." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swpx = swpx.assign({\"CBB\": ([\"azimuth\", \"range\"], CBB)})\n", + "# recalculate georeferencing for AEQD\n", + "swpx = swpx.pipe(wrl.georef.georeference_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(12, 4))\n", + "\n", + "ax1 = fig.add_subplot(121)\n", + "swpx.reflectivity.plot(x=\"x\", y=\"y\", ax=ax1, cmap=\"turbo\", vmin=0, vmax=60)\n", + "ax1.set_title(f\"Signal Processor - {swpx.time.values.astype('M8[s]')}\")\n", + "ax1.set_aspect(\"equal\")\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "# CBB > 0.5, CMAP == 1, RHOHV < 0.8 is masked\n", + "swpx.where(\n", + " (swpx.CBB <= 0.5)\n", + " & (swpx.CMAP < 1.0)\n", + " & (swpx.uncorrected_cross_correlation_ratio >= 0.8)\n", + ").reflectivity_hh_clut.plot(x=\"x\", y=\"y\", ax=ax2, cmap=\"turbo\", vmin=0, vmax=60)\n", + "ax2.set_title(f\"Gabella+CBB+RHOHV - {swpx.time.values.astype('M8[s]')}\")\n", + "ax2.set_aspect(\"equal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "We've just learned how to use $\\omega radlib$'s Gabella clutter detection for single sweeps. Wrapping numpy based functions for use with `xarray.apply_ufunc` has been shown. We've looked into digital elevation maps and beam blockage calculations.\n", + "\n", + "### What's next?\n", + "In the next notebook we dive into processing of differential phase." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and references\n", + "\n", + "- [SRTM - NASA - EARTHDATA](https://urs.earthdata.nasa.gov/)\n", + "- [xarray](https://docs.xarray.dev)\n", + "- [apply_ufunc](https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html)\n", + "- [dask](https://docs.dask.org/)\n", + "- [gdal](https://gdal.org/index.html)\n", + "- [hvplot](https://hvplot.holoviz.org/)\n", + "- [wradlib xarray backends](https://docs.wradlib.org/projects/old-docs/en/1.16.0/notebooks/fileio/wradlib_xarray_backends.html)\n", + "- [CfRadial1](https://ncar.github.io/CfRadial/)\n", + "- [OPERA ODIM_H5](https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/)\n", + "- [WMO JET-OWR](https://community.wmo.int/governance/commission-membership/commission-observation-infrastructure-and-information-systems-infcom/commission-infrastructure-officers/infcom-management-group/standing-committee-measurements-instrumentation-and-traceability-sc-mint/joint-expert-team)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "nbdime-conflicts": { + "local_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python 3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ], + "remote_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ] + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/wradlib/wradlib_differential_phase.ipynb b/_preview/104/_sources/notebooks/wradlib/wradlib_differential_phase.ipynb new file mode 100644 index 00000000..b9dce850 --- /dev/null +++ b/_preview/104/_sources/notebooks/wradlib/wradlib_differential_phase.ipynb @@ -0,0 +1,1137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"wradlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# wradlib Phase Processing - System PhiDP - ZPHI-Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Within this notebook, we will cover:\n", + "\n", + "1. Reading sweep data into xarray based Dataset\n", + "1. Retrieval of system PhiDP\n", + "1. ZPHI Phase processing\n", + "1. Attenuation correction using specific Attenuation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Xarray Basics](https://tutorial.xarray.dev/intro.html) | Helpful | Basic Dataset/DataArray |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic Plotting |\n", + "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Projections |\n", + "\n", + "- **Time to learn**: 10 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime as dt\n", + "import glob\n", + "import os\n", + "import sys\n", + "import warnings\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "from scipy.integrate import cumulative_trapezoid\n", + "\n", + "import wradlib as wrl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data\n", + "\n", + "As a quick example to show the algorithm, we use a file from Down Under. For the further processing we us XBand data from BoXPol research radar at the University of Bonn, Germany." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "boxpol = \"data/hdf5/boxpol/2014-11-16--03:45:00,00.mvol\"\n", + "terrey = \"data/hdf5/terrey_39.h5\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swp0 = wrl.io.open_odim_dataset(terrey)[0]\n", + "swp0 = swp0.pipe(wrl.georef.georeference_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "System PHIDP aka Phase Offset\n", + "\n", + "The following function returns phase offset as well as start and stop ranges of the region of interest (first precipitating bins)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def phase_offset(phioff, method=None, rng=3000.0, npix=None, **kwargs):\n", + " \"\"\"Calculate Phase offset.\n", + "\n", + " Parameter\n", + " ---------\n", + " phioff : xarray.DataArray\n", + " differential phase DataArray\n", + "\n", + " Keyword Arguments\n", + " -----------------\n", + " method : str\n", + " aggregation method, defaults to 'median'\n", + " rng : float\n", + " range in m to calculate system phase offset\n", + "\n", + " Return\n", + " ------\n", + " phidp_offset : xarray.Dataset\n", + " Dataset with PhiDP offset and start/stop ranges\n", + " \"\"\"\n", + " range_step = np.diff(phioff.range)[0]\n", + " nprec = int(rng / range_step)\n", + " if nprec % 2:\n", + " nprec += 1\n", + "\n", + " if npix is None:\n", + " npix = nprec // 2 + 1\n", + "\n", + " # create binary array\n", + " phib = xr.where(np.isnan(phioff), 0, 1)\n", + "\n", + " # take nprec range bins and calculate sum\n", + " phib_sum = phib.rolling(range=nprec, **kwargs).sum(skipna=True)\n", + "\n", + " # find at least N pixels in\n", + " # phib_sum_N = phib_sum.where(phib_sum >= npix)\n", + " phib_sum_N = xr.where(phib_sum <= npix, phib_sum, npix)\n", + "\n", + " # get start range of first N consecutive precip bins\n", + " start_range = (\n", + " phib_sum_N.idxmax(dim=\"range\") - nprec // 2 * np.diff(phib_sum.range)[0]\n", + " )\n", + " start_range = xr.where(start_range < 0, 0, start_range)\n", + "\n", + " # get stop range\n", + " stop_range = start_range + rng\n", + " # get phase values in specified range\n", + " off = phioff.where(\n", + " (phioff.range >= start_range) & (phioff.range <= stop_range), drop=False\n", + " )\n", + " # calculate nan median over range\n", + " if method is None:\n", + " method = \"median\"\n", + " func = getattr(off, method)\n", + " off_func = func(dim=\"range\", skipna=True)\n", + "\n", + " return xr.Dataset(\n", + " dict(\n", + " PHIDP_OFFSET=off_func,\n", + " start_range=start_range,\n", + " stop_range=stop_range,\n", + " phib_sum=phib_sum,\n", + " phib=phib,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Showcase " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dr_m = swp0.range.diff(\"range\").median()\n", + "swp_msk = swp0.where((swp0.DBZH >= 0.0))\n", + "swp_msk = swp_msk.where(swp_msk.RHOHV > 0.8)\n", + "swp_msk = swp_msk.where(swp_msk.range > dr_m * 5)\n", + "\n", + "phi_masked = swp_msk.PHIDP.copy()\n", + "off = phase_offset(\n", + " phi_masked, method=\"median\", rng=2000.0, npix=7, center=True, min_periods=4\n", + ")\n", + "phioff = off.PHIDP_OFFSET.median(dim=\"azimuth\", skipna=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16, 7))\n", + "ax1 = plt.subplot(111, projection=\"polar\")\n", + "# set the lable go clockwise and start from the top\n", + "ax1.set_theta_zero_location(\"N\")\n", + "# clockwise\n", + "ax1.set_theta_direction(-1)\n", + "theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)\n", + "ax1.plot(theta, off.PHIDP_OFFSET, color=\"b\", linewidth=3)\n", + "\n", + "ax1.plot(theta, np.ones_like(theta) * phioff.values, color=\"r\", lw=2)\n", + "ti = off.time.values.astype(\"M8[s]\")\n", + "om = phioff.values\n", + "tx = ax1.set_title(f\"{ti}\\n\" + r\"$\\phi_{DP}-Offset$ \" + f\"{om:.1f} (deg)\")\n", + "tx.set_y(1.1)\n", + "xticks = ax1.set_xticks(np.pi / 180.0 * np.linspace(0, 360, 36, endpoint=False))\n", + "ax1.set_ylim(50, 150)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(18, 5))\n", + "swp_msk.DBZH.plot(x=\"azimuth\")\n", + "off.start_range.plot(c=\"b\", lw=2)\n", + "off.stop_range.plot(c=\"r\", lw=2)\n", + "plt.gca().set_ylim(0, 25000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Process BoXPol data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = wrl.io.open_gamic_dataset(boxpol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(vol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swp = vol[0].copy()\n", + "swp = swp.pipe(wrl.georef.georeference_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(swp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(13, 5))\n", + "\n", + "ax1 = fig.add_subplot(121)\n", + "im1 = swp.PHIDP.where(swp.RHOHV > 0.8).plot(x=\"x\", y=\"y\", ax=ax1, cmap=\"turbo\")\n", + "t = plt.title(r\"Uncorrected $\\phi_{DP}$\")\n", + "t.set_y(1.1)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "im2 = swp.DBZH.where(swp.RHOHV > 0.8).plot(\n", + " x=\"x\", y=\"y\", ax=ax2, cmap=\"turbo\", vmin=-10, vmax=50\n", + ")\n", + "t = plt.title(r\"Uncorrected $Z_{H}$\")\n", + "t.set_y(1.1)\n", + "fig.suptitle(swp.time.values, fontsize=14)\n", + "fig.subplots_adjust(wspace=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply reasonable masking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dr_m = swp.range.diff(\"range\").median()\n", + "swp_msk = swp.where((swp.DBZH >= 0.0))\n", + "swp_msk = swp_msk.where(swp_msk.RHOHV > 0.8)\n", + "swp_msk = swp_msk.where(swp_msk.range > dr_m * 2)\n", + "\n", + "\n", + "phi_masked = swp_msk.PHIDP.copy()\n", + "off = phase_offset(\n", + " phi_masked, method=\"median\", rng=2000.0, npix=7, center=True, min_periods=2\n", + ")\n", + "phioff = off.PHIDP_OFFSET.median(dim=\"azimuth\", skipna=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot phase offset distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16, 7))\n", + "ax1 = plt.subplot(111, projection=\"polar\")\n", + "# set the lable go clockwise and start from the top\n", + "ax1.set_theta_zero_location(\"N\")\n", + "# clockwise\n", + "ax1.set_theta_direction(-1)\n", + "theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)\n", + "ax1.plot(theta, off.PHIDP_OFFSET, color=\"b\", linewidth=3)\n", + "\n", + "ax1.plot(theta, np.ones_like(theta) * phioff.values, color=\"r\", lw=2)\n", + "ti = off.time.values.astype(\"M8[s]\")\n", + "om = phioff.values\n", + "tx = ax1.set_title(f\"{ti}\\n\" + r\"$\\phi_{DP}-Offset$ \" + f\"{om:.1f} (deg)\")\n", + "tx.set_y(1.1)\n", + "xticks = ax1.set_xticks(np.pi / 180.0 * np.linspace(0, 360, 36, endpoint=False))\n", + "ax1.set_ylim(-120, -70)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(18, 5))\n", + "swp_msk.DBZH.plot(x=\"azimuth\")\n", + "off.start_range.plot(c=\"b\", lw=2)\n", + "off.stop_range.plot(c=\"r\", lw=2)\n", + "plt.gca().set_ylim(0, 10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pleaser refer to the [ZPHI-Method](#ZPHI-Method) section at the bottom of this notebook for references and equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieving $\\Delta \\phi_{DP}$\n", + "\n", + "We will use the simple method of finding the first and the last non NAN values per ray from $\\phi_{DP}^{corr}$.\n", + "\n", + "This is the most simple and probably not very robust method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def phase_zphi(phi, rng=1000.0, **kwargs):\n", + " range_step = np.diff(phi.range)[0]\n", + "\n", + " nprec = int(rng / range_step)\n", + "\n", + " if nprec % 2:\n", + " nprec += 1\n", + "\n", + " # create binary array\n", + " phib = xr.where(np.isnan(phi), 0, 1)\n", + "\n", + " # take nprec range bins and calculate sum\n", + " phib_sum = phib.rolling(range=nprec, **kwargs).sum(skipna=True)\n", + "\n", + " offset = nprec // 2 * np.diff(phib_sum.range)[0]\n", + " offset_idx = nprec // 2\n", + "\n", + " start_range = phib_sum.idxmax(dim=\"range\") - offset\n", + " start_range_idx = phib_sum.argmax(dim=\"range\") - offset_idx\n", + "\n", + " stop_range = phib_sum[:, ::-1].idxmax(dim=\"range\") - offset\n", + " stop_range_idx = (\n", + " len(phib_sum.range) - (phib_sum[:, ::-1].argmax(dim=\"range\") - offset_idx) - 2\n", + " )\n", + "\n", + " # get phase values in specified range\n", + " first = phi.where(\n", + " (phi.range >= start_range) & (phi.range <= start_range + rng), drop=True\n", + " ).quantile(0.15, dim=\"range\", skipna=True)\n", + " last = phi.where(\n", + " (phi.range >= stop_range - rng) & (phi.range <= stop_range), drop=True\n", + " ).quantile(0.95, dim=\"range\", skipna=True)\n", + "\n", + " return xr.Dataset(\n", + " dict(\n", + " phib=phib_sum,\n", + " offset=offset,\n", + " offset_idx=offset_idx,\n", + " start_range=start_range,\n", + " stop_range=stop_range,\n", + " first=first.drop(\"quantile\"),\n", + " first_idx=start_range_idx,\n", + " last=last.drop(\"quantile\"),\n", + " last_idx=stop_range_idx,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply extraction of phase parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cphase = phase_zphi(swp_msk.PHIDP, rng=2000.0, center=True, min_periods=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply azimuthal averaging." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cphase = (\n", + " cphase.pad(pad_width={\"azimuth\": 2}, mode=\"wrap\")\n", + " .rolling(azimuth=5, center=True)\n", + " .median(skipna=True)\n", + " .isel(azimuth=slice(2, -2))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $\\Delta \\phi_{DP}$ - Polar Plots\n", + "\n", + "This visualizes `first` and `last` indizes including $\\Delta \\phi_{DP}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dphi = cphase.last - cphase.first\n", + "dphi = dphi.where(dphi >= 0).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(20, 9))\n", + "ax1 = plt.subplot(131, projection=\"polar\")\n", + "ax2 = plt.subplot(132, projection=\"polar\")\n", + "ax3 = plt.subplot(133, projection=\"polar\")\n", + "# set the lable go clockwise and start from the top\n", + "ax1.set_theta_zero_location(\"N\")\n", + "ax2.set_theta_zero_location(\"N\")\n", + "ax3.set_theta_zero_location(\"N\")\n", + "# clockwise\n", + "ax1.set_theta_direction(-1)\n", + "ax2.set_theta_direction(-1)\n", + "ax3.set_theta_direction(-1)\n", + "theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)\n", + "ax1.plot(theta, cphase.start_range, color=\"b\", linewidth=2)\n", + "ax1.plot(theta, cphase.stop_range, color=\"r\", linewidth=2)\n", + "_ = ax1.set_title(\"Start/Stop Range\")\n", + "\n", + "ax2.plot(theta, cphase.first, color=\"b\", linewidth=2)\n", + "ax2.plot(theta, cphase.last, color=\"r\", linewidth=2)\n", + "_ = ax2.set_title(\"Start/Stop PHIDP\")\n", + "ax2.set_ylim(-110, -40)\n", + "\n", + "ax3.plot(theta, dphi, color=\"g\", linewidth=3)\n", + "# ax3.plot(theta, dphi_old, color=\"k\", linewidth=1)\n", + "_ = ax3.set_title(\"Delta PHIDP\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating $f\\Delta\\phi_{DP}$\n", + "\n", + "$$f\\Delta\\phi_{DP} = 10^{0.1 \\cdot b \\cdot \\alpha \\cdot \\Delta\\phi_{DP}} - 1$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# todo: cband coeffizienten\n", + "alphax = 0.28\n", + "betax = 0.05\n", + "bx = 0.78\n", + "# need to expand alphax to dphi-shape\n", + "fdphi = 10 ** (0.1 * bx * alphax * dphi) - 1\n", + "fdphi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Reflectivity Integrals/Sums\n", + "\n", + "$$za(r) = \\left[Z_a(r) \\right ]^b$$\n", + "\n", + "$$iza(r,r2) = 0.46 \\cdot b \\cdot \\int_{r}^{r2} \\left [Z_a(s) \\right ]^b ds$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do not restrict (mask) the reflectivities for now, but switch between `DBTH` and `DBZH` to see the difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zhraw = swp.DBZH.where(\n", + " (swp.range > cphase.start_range) & (swp.range < cphase.stop_range)\n", + ")\n", + "zhraw.plot(x=\"x\", y=\"y\", cmap=\"turbo\", vmin=0, vmax=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate linear reflectivity and ^b\n", + "zax = zhraw.pipe(wrl.trafo.idecibel).fillna(0)\n", + "za = zax**bx\n", + "# set masked to zero for integration\n", + "za_zero = za.fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate cumulative integral, and subtract from maximum. That way we have the cumulative sum for every bin until the end of the ray." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cumulative_trapezoid_xarray(da, dim, initial=0):\n", + " \"\"\"Intgration with the scipy.integrate.cumtrapz.\n", + "\n", + " Parameter\n", + " ---------\n", + " da : xarray.DataArray\n", + " array with differential phase data\n", + " dim : int\n", + " size of window in range dimension\n", + "\n", + " Keyword Arguments\n", + " -----------------\n", + " initial : float\n", + " minimum number of valid bins\n", + "\n", + " Return\n", + " ------\n", + " kdp : xarray.DataArray\n", + " DataArray with specific differential phase values\n", + " \"\"\"\n", + " x = da[dim]\n", + " dx = x.diff(dim).median(dim).values\n", + " if x.attrs[\"units\"] == \"meters\":\n", + " dx /= 1000.0\n", + " return xr.apply_ufunc(\n", + " cumulative_trapezoid,\n", + " da,\n", + " input_core_dims=[[dim]],\n", + " output_core_dims=[[dim]],\n", + " dask=\"parallelized\",\n", + " kwargs=dict(axis=da.get_axis_num(dim), initial=initial, dx=dx),\n", + " dask_gufunc_kwargs=dict(allow_rechunk=True),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iza_x = 0.46 * bx * za_zero.pipe(cumulative_trapezoid_xarray, \"range\", initial=0)\n", + "iza = iza_x.max(\"range\") - iza_x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating Attenuation $A_{H}$ for whole domain\n", + "\n", + "$$A_{H}(r) = \\frac{\\left [Z_a(r) \\right ]^b \\cdot f(\\Delta \\phi_{DP})}{0.46b \\int_{r1}^{r2} \\left [Z_a(s) \\right ]^b ds + f(\\Delta \\phi_{DP}) \\cdot 0.46b \\int_{r}^{r2} \\left [Z_a(s) \\right ]^b ds}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can reduce the number of operations by rearranging the equation like this:\n", + "\n", + "$$A_{H}(r) = \\frac{\\left [Z_a(r) \\right ]^b}{\\frac{0.46b \\int_{r1}^{r2} \\left [Z_a(s) \\right ]^b ds}{f(\\Delta \\phi_{DP})} + 0.46b \\int_{r}^{r2} \\left [Z_a(s) \\right ]^b ds}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iza_fdphi = iza / fdphi\n", + "idx = cphase.first_idx.astype(int)\n", + "iza_first = iza_fdphi[:, idx]\n", + "ah = za / (iza_first + iza)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Give it a name!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ah.name = \"AH\"\n", + "ah.attrs[\"short_name\"] = \"specific_attenuation_h\"\n", + "ah.attrs[\"long_name\"] = \"Specific attenuation H\"\n", + "ah.attrs[\"units\"] = \"dB/km\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111)\n", + "ticks_ah = np.arange(0, 5, 0.2)\n", + "im = ah.plot(x=\"x\", y=\"y\", ax=ax, cmap=\"turbo\", levels=np.arange(0, 0.5, 0.025))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate $\\phi_{DP}^{cal}(r, \\alpha)$ for whole domain\n", + "\n", + "$$\\phi_{DP}^{cal}(r_i, \\alpha) = 2 \\cdot \\int_{r1}^{r2} \\frac{A_H(s; \\alpha)}{\\alpha}ds$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "phical = 2 * (ah / alphax).pipe(cumulative_trapezoid_xarray, \"range\", initial=0)\n", + "phical.name = \"PHICAL\"\n", + "phical.attrs = wrl.io.xarray.moments_mapping[\"PHIDP\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "phical.where(swp_msk.PHIDP).plot(x=\"x\", y=\"y\", vmin=0, vmax=50, cmap=\"turbo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply attenuation correction \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(alphax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zhraw = swp.DBZH.copy()\n", + "zdrraw = swp.ZDR.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with xr.set_options(keep_attrs=True):\n", + " zhcorr = zhraw + alphax * (phical)\n", + " zdiff = zhcorr - zhraw\n", + " zdrcorr = zdrraw + betax * (phical)\n", + " zdrdiff = zdrcorr - zdrraw" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(\n", + " nrows=2,\n", + " ncols=2,\n", + " figsize=(15, 12),\n", + " sharex=True,\n", + " sharey=True,\n", + " squeeze=True,\n", + " constrained_layout=True,\n", + ")\n", + "\n", + "scantime = zhraw.time.values.astype(\"" + ] + }, + { + "cell_type": "markdown", + "id": "ffd54e5c", + "metadata": {}, + "source": [ + "# An Overview of wradlib\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "3bf46aa2", + "metadata": {}, + "source": [ + "## wradlib Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "189e6222-2d21-4046-af89-d6d0b59fc066", + "metadata": {}, + "source": [ + "$\\omega radlib$ was one of the first available free and open source Python packages which was targeting the whole processing chain of weather radar data.\n", + "\n", + "From the beginning in 2011 it is available for collaboration in the cloud, first at bitbucket and from 2016 at it's current location at [github](https://github.com/wradlib/wradlib).\n", + "\n", + "$\\omega radlib$ evolved constantly over time also adapting to new and emerging features of the Scientific Python stack. Many Features have been added ever since and $\\omega radlib$ is used in almost all parts of the world.\n", + "\n", + "$\\omega radlib$'s development paradigm __Keep the magic to a minimum__ with a transparent, but lower level code is still a main goal of all development activities. Also the __flat (or no) data model__ with passing data as numpy arrays and metadata as dictionaries is up to this time base for many functions in $\\omega radlib$. With the adoption of the emerging Xarray package this changed to some extent, combining data and metadata in a convenient way.\n", + "\n", + "In this short course we will concentrate on:\n", + "\n", + "1. [reading, exploring and exporting radar data, gridding and gis export](wradlib_radar_data_io_vis.ipynb)\n", + "1. [data quality and beam blockage](wradlib_data_quality.ipynb)\n", + "1. [processing of differential phase](wradlib_differential_phase.ipynb)\n", + "1. [quasi vertical profiles](wradlib_quasi_vertical_profiles.ipynb)\n", + "\n", + "For a more comprehensive set of examples and tutorials please refer to the [$\\omega radlib$ documentation](https://docs.wradlib.org/en/stable/index.html).\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/104/_sources/notebooks/wradlib/wradlib_quasi_vertical_profiles.ipynb b/_preview/104/_sources/notebooks/wradlib/wradlib_quasi_vertical_profiles.ipynb new file mode 100644 index 00000000..313c5bef --- /dev/null +++ b/_preview/104/_sources/notebooks/wradlib/wradlib_quasi_vertical_profiles.ipynb @@ -0,0 +1,338 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"wradlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# wradlib time series data and quasi vertical profiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Within this notebook, we will cover:\n", + "\n", + "1. Reading radar sweep timeseries data into xarray based RadarVolume\n", + "1. Examination of RadarVolume and Sweep\n", + "1. Calculation of Quasivertical Profiles and Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic Plotting |\n", + "| [Xarray Basics](https://tutorial.xarray.dev/intro.html) | Helpful | Basic Dataset/DataArray |\n", + "| [Xarray Plotting](https://tutorial.xarray.dev/intro.html) | Helpful | Basic Plotting/Faceting |\n", + "\n", + "- **Time to learn**: 7.5 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "from tqdm import tqdm_notebook as tqdm\n", + "\n", + "import wradlib as wrl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Import Australian Radar Data\n", + "\n", + "It is assumed, that data from IDR71 (Terrey Hills, Sidney) from 20th of December 2018 is used in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fglob = \"data/hdf5/terrey_*.h5\"\n", + "idr71 = glob.glob(fglob)\n", + "idr71.sort()\n", + "print(\"Files available: {}\".format(len(idr71)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single Quasi Vertical Profile (QVP)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "odh = wrl.io.open_odim_dataset(idr71[24])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(odh)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example shows how to create a so called QVP. We need to define a function to add a height coordinate for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add_height(ds):\n", + " ds = ds.pipe(wrl.georef.georeference_dataset)\n", + " height = ds.z.mean(\"azimuth\")\n", + " ds = ds.assign_coords({\"height\": ([\"range\"], height.data)})\n", + " return ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we add the height coordinate and calculate the `mean` over the azimuth using the sweep with the highest available elevation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swp = odh[0].pipe(add_height)\n", + "display(swp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qvp = swp.mean(\"azimuth\")\n", + "qvp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qvp.DBZH.plot(y=\"height\", figsize=(5, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TimeSeries QVP\n", + "\n", + "All wradlib xarray backends have the capability to read multiple sweeps/volumes in one go. We have to prepare the list of files a bit, though." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ts = xr.open_mfdataset(\n", + " idr71,\n", + " engine=\"odim\",\n", + " group=\"dataset1\",\n", + " combine=\"nested\",\n", + " concat_dim=\"time\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Georeference and add height coordinate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ts = ts.pipe(add_height)\n", + "display(ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stats = [\"median\", \"mean\", \"min\", \"max\"]\n", + "stat = [\n", + " getattr(ts.where(ts.RHOHV > 0.8), st)(\"azimuth\", skipna=True, keep_attrs=True)\n", + " for st in stats\n", + "]\n", + "ts_stats = xr.concat(stat, dim=\"stats\")\n", + "ts_stats = ts_stats.assign_coords({\"stats\": stats})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(ts_stats)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot QVP's" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "levels = np.arange(-30, 80, 5)\n", + "facet = ts_stats.TH.plot(\n", + " x=\"time\",\n", + " y=\"height\",\n", + " col=\"stats\",\n", + " col_wrap=2,\n", + " cmap=\"turbo\",\n", + " figsize=(12, 10),\n", + " levels=levels,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "Easy creation of Quasi Vertical Profiles was shown.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and references\n", + "\n", + "- [xarray](https://docs.xarray.dev)\n", + "- [dask](https://docs.dask.org/)\n", + "- [wradlib xarray backends](https://docs.wradlib.org/projects/old-docs/en/1.16.0/notebooks/fileio/wradlib_xarray_backends.html)\n", + "- [OPERA ODIM_H5](https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/)\n", + "- [WMO JET-OWR](https://community.wmo.int/governance/commission-membership/commission-observation-infrastructure-and-information-systems-infcom/commission-infrastructure-officers/infcom-management-group/standing-committee-measurements-instrumentation-and-traceability-sc-mint/joint-expert-team)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/notebooks/wradlib/wradlib_radar_data_io_vis.ipynb b/_preview/104/_sources/notebooks/wradlib/wradlib_radar_data_io_vis.ipynb new file mode 100644 index 00000000..2da0a908 --- /dev/null +++ b/_preview/104/_sources/notebooks/wradlib/wradlib_radar_data_io_vis.ipynb @@ -0,0 +1,823 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"wradlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# wradlib radar data io, visualisation, gridding and gis export" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Within this notebook, we will cover:\n", + "\n", + "1. Reading radar volume data into xarray based RadarVolume\n", + "1. Examination of RadarVolume and Sweeps\n", + "1. Plotting of sweeps, simple and mapmaking\n", + "1. Gridding and GIS output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Xarray Basics](https://tutorial.xarray.dev/intro.html) | Helpful | Basic Dataset/DataArray |\n", + "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Helpful | Basic Plotting |\n", + "| [Cartopy Basics](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Helpful | Projections |\n", + "| [GDAL Basiscs](https://gdal.org/api/python_bindings.html) | Helpful | Raster |\n", + "\n", + "- **Time to learn**: 15 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import pathlib\n", + "\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib import ticker as tick\n", + "from osgeo import gdal\n", + "\n", + "import wradlib as wrl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data into RadarVolume\n", + "\n", + "We have this special case here with Rainbow data where moments are splitted across files. Each file nevertheless consists of all sweeps comprising the volume. We'll use some special nested ordering to read the files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fglob = \"data/rainbow/meteoswiss/*.vol\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol = wrl.io.open_rainbow_mfdataset(fglob, combine=\"by_coords\", concat_dim=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examine RadarVolume\n", + "\n", + "The RadarVolume is a shallow class which tries to comply to CfRadial2/WMO-FM301, see [WMO-CF_Extensions](https://community.wmo.int/activity-areas/wis/wmo-cf-extensions).\n", + "\n", + "The printout of `RadarVolume` just lists the dimensions and the associated elevations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(vol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Root Group\n", + "\n", + "The root-group is essentially an overview over the volume, more or less aligned with CfRadial metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol.root" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sweep Groups\n", + "\n", + "Sweeps are available in a sequence attached to the `RadarVolume` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "swp = vol[0]\n", + "display(swp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect Scan Strategy\n", + "\n", + "Considering volume files it's nice to have an overview over the scan strategy. We can choose some reasonable values for the layout." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nrays = 360\n", + "nbins = 150\n", + "range_res = 1000.0\n", + "ranges = np.arange(nbins) * range_res\n", + "elevs = vol.root.sweep_fixed_angle.values\n", + "sitecoords = (\n", + " vol.root.longitude.values.item(),\n", + " vol.root.latitude.values.item(),\n", + " vol.root.altitude.values.item(),\n", + ")\n", + "\n", + "beamwidth = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = wrl.vis.plot_scan_strategy(ranges, elevs, sitecoords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot it on top of the terrain derived from SRTM DEM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"WRADLIB_EARTHDATA_BEARER_TOKEN\"] = \"\"\n", + "os.environ[\"WRADLIB_DATA\"] = \"data/wradlib-data\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = wrl.vis.plot_scan_strategy(ranges, elevs, sitecoords, terrain=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make the earth go round..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = wrl.vis.plot_scan_strategy(\n", + " ranges, elevs, sitecoords, cg=True, terrain=True, az=180\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Radar Data\n", + "### Time vs. Azimuth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 5))\n", + "ax1 = fig.add_subplot(111)\n", + "swp.azimuth.sortby(\"rtime\").plot(x=\"rtime\", marker=\".\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Range vs. Azimuth/Time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 5))\n", + "ax1 = fig.add_subplot(121)\n", + "swp.DBZH.plot(cmap=\"turbo\", ax=ax1)\n", + "ax1.set_title(f\"{swp.time.values.astype('M8[s]')}\")\n", + "ax2 = fig.add_subplot(122)\n", + "swp.DBZH.sortby(\"rtime\").plot(y=\"rtime\", cmap=\"turbo\", ax=ax2)\n", + "ax2.set_title(f\"{swp.time.values.astype('M8[s]')}\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Georeferenced as Plan Position Indicator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 10))\n", + "ax1 = fig.add_subplot(111)\n", + "swp.DBZH.pipe(wrl.georef.georeference_dataset).plot(\n", + " x=\"x\", y=\"y\", ax=ax1, cmap=\"turbo\", cbar_kwargs=dict(shrink=0.8)\n", + ")\n", + "ax1.plot(0, 0, \"rx\", markersize=12)\n", + "ax1.set_title(f\"{swp.time.values.astype('M8[s]')}\")\n", + "ax1.grid()\n", + "ax1.set_aspect(\"equal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic MapMaking with cartopy\n", + "\n", + "The data will be georeferenced as `Azimuthal Equidistant Projection` centered at the radar. For the map projection we will use `Mercator`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "map_trans = ccrs.AzimuthalEquidistant(\n", + " central_latitude=swp.latitude.values, central_longitude=swp.longitude.values\n", + ")\n", + "map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_borders(ax):\n", + " borders = cfeature.NaturalEarthFeature(\n", + " category=\"cultural\", name=\"admin_0_countries\", scale=\"10m\", facecolor=\"none\"\n", + " )\n", + " ax.add_feature(borders, edgecolor=\"black\", lw=2, zorder=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 8))\n", + "ax = fig.add_subplot(111, projection=map_proj)\n", + "cbar_kwargs = dict(shrink=0.7, pad=0.075)\n", + "pm = swp.DBZH.pipe(wrl.georef.georeference_dataset).plot(\n", + " ax=ax, x=\"x\", y=\"y\", cbar_kwargs=cbar_kwargs, cmap=\"turbo\", transform=map_trans\n", + ")\n", + "plot_borders(ax)\n", + "ax.gridlines(draw_labels=True)\n", + "ax.plot(\n", + " swp.longitude.values, swp.latitude.values, transform=map_trans, marker=\"*\", c=\"r\"\n", + ")\n", + "ax.set_title(f\"{swp.time.values.astype('M8[s]')}\")\n", + "ax.set_xlim(-15e4, 45e4)\n", + "ax.set_ylim(565e4, 610e4)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot on curvelinear grid\n", + "\n", + "For Xarray DataArrays wradlib uses a so-called accessor (`wradlib`). To plot on curvelinear grids projection has to be set to `cg`, which uses the matplotlib AXISARTIS namespace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10, 8))\n", + "\n", + "pm = swp.DBZH.pipe(wrl.georef.georeference_dataset).wradlib.plot(\n", + " proj=\"cg\", fig=fig, cmap=\"turbo\"\n", + ")\n", + "\n", + "ax = plt.gca()\n", + "\n", + "# apply eye-candy\n", + "caax = ax.parasites[0]\n", + "paax = ax.parasites[1]\n", + "ax.parasites[1].set_aspect(\"equal\")\n", + "t = plt.title(f\"{vol[0].time.values.astype('M8[s]')}\", y=1.05)\n", + "cbar = plt.colorbar(pm, pad=0.075, ax=paax)\n", + "caax.set_xlabel(\"x_range [m]\")\n", + "caax.set_ylabel(\"y_range [m]\")\n", + "plt.text(1.0, 1.05, \"azimuth\", transform=caax.transAxes, va=\"bottom\", ha=\"right\")\n", + "cbar.set_label(\"reflectivity [dBZ]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ODIM_H5 format export and import\n", + "### Export to ODIM_H5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol.to_odim(\"test_odim_vol.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import from ODIM_H5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vol2 = wrl.io.open_odim_dataset(\"test_odim_vol.h5\")\n", + "display(vol2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(vol2[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import with xarray backends\n", + "\n", + "We can facilitate the xarray backend's which wradlib provides for the different readers. The xarray backends are capable of loading data into a single Dataset for now. So we need to give some information here too.\n", + "\n", + "### Open single files\n", + "\n", + "The simplest case can only open one file and one group a time!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_dataset(\"test_odim_vol.h5\", engine=\"odim\", group=\"dataset1\")\n", + "display(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Open multiple files\n", + "\n", + "Here we just specify the group, which in case of rainbow files is given by the group number." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_mfdataset(fglob, engine=\"rainbow\", group=0, combine=\"by_coords\")\n", + "display(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gridding and Export to GIS formats\n", + "\n", + "- get coordinates from source Dataset with given projection\n", + "- calculate target coordinates\n", + "- grid using wradlib interpolator\n", + "- export to single band geotiff\n", + "- use GDAL CLI tools to convert to grayscaled/paletted PNG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_target_grid(ds, nb_pixels):\n", + " xgrid = np.linspace(ds.x.min(), ds.x.max(), nb_pixels, dtype=np.float32)\n", + " ygrid = np.linspace(ds.y.min(), ds.y.max(), nb_pixels, dtype=np.float32)\n", + " grid_xy_raw = np.meshgrid(xgrid, ygrid)\n", + " grid_xy_grid = np.dstack((grid_xy_raw[0], grid_xy_raw[1]))\n", + " return xgrid, ygrid, grid_xy_grid\n", + "\n", + "\n", + "def get_target_coordinates(grid):\n", + " grid_xy = np.stack((grid[..., 0].ravel(), grid[..., 1].ravel()), axis=-1)\n", + " return grid_xy\n", + "\n", + "\n", + "def get_source_coordinates(ds):\n", + " xy = np.stack((ds.x.values.ravel(), ds.y.values.ravel()), axis=-1)\n", + " return xy\n", + "\n", + "\n", + "def coordinates(da, proj, res=100):\n", + " # georeference single sweep\n", + " da = da.pipe(wrl.georef.georeference_dataset, proj=proj)\n", + " # get source coordinates\n", + " src = get_source_coordinates(da)\n", + " # create target grid\n", + " xgrid, ygrid, trg = get_target_grid(da, res)\n", + " return src, trg\n", + "\n", + "\n", + "def moment_to_gdal(da, trg_grid, driver, ext, path=\"\", proj=None):\n", + " # use wgs84 pseudo mercator if no projection is given\n", + " if proj is None:\n", + " proj = wrl.georef.epsg_to_osr(3857)\n", + " t = da.time.values.astype(\"M8[s]\").astype(\"O\")\n", + " outfilename = f\"gridded_{da.name}_{t:%Y%m%d}_{t:%H%M%S}\"\n", + " outfilename = os.path.join(path, outfilename)\n", + " f = pathlib.Path(outfilename)\n", + " f.unlink(missing_ok=True)\n", + " res = ip_near(da.values.ravel(), maxdist=1000).reshape(\n", + " (len(trg_grid[0]), len(trg_grid[1]))\n", + " )\n", + " data, xy = wrl.georef.set_raster_origin(res, trg_grid, \"upper\")\n", + " ds = wrl.georef.create_raster_dataset(data, xy, projection=proj)\n", + " wrl.io.write_raster_dataset(outfilename + ext, ds, driver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "epsg_code = 2056\n", + "proj = wrl.georef.epsg_to_osr(epsg_code)\n", + "src, trg = coordinates(ds, proj, res=1400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpolator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "ip_near = wrl.ipol.Nearest(src, trg.reshape(-1, trg.shape[-1]), remove_missing=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gridding and Export" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "moment_to_gdal(ds.DBZH, trg, \"GTiff\", \".tif\", proj=proj)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GDAL info on created GeoTiff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!gdalinfo gridded_DBZH_20191021_082409.tif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translate exported GeoTiff to grayscale PNG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!gdal_translate -of PNG -ot Byte -scale -30. 60. 0 255 gridded_DBZH_20191021_082409.tif grayscale.png" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply colortable to PNG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"colors.txt\", \"w\") as f:\n", + " f.write(\"0 blue\\n\")\n", + " f.write(\"50 yellow\\n\")\n", + " f.write(\"100 yellow\\n\")\n", + " f.write(\"150 orange\\n\")\n", + " f.write(\"200 red\\n\")\n", + " f.write(\"250 white\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display exported PNG's" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!gdaldem color-relief grayscale.png colors.txt paletted.png" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"grayscale\n", + "\"paletted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import with Xarray, rasterio backend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with xr.open_dataset(\"gridded_DBZH_20191021_082409.tif\", engine=\"rasterio\") as ds_grd:\n", + " display(ds_grd)\n", + " ds_grd.band_data.plot(cmap=\"turbo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "We've just learned how to use $\\omega radlib$'s xarray backends to make radar volume data available as xarray Datasets and DataArrays. Accessing, plotting and exporting data has been shown.\n", + "\n", + "### What's next?\n", + "In the next notebook we dive into data quality processing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and references\n", + "\n", + "- [xarray](https://docs.xarray.dev)\n", + "- [dask](https://docs.dask.org)\n", + "- [matplotlib](https://matplotlib.org/stable/index.html)\n", + "- [matplotlib axisartist](https://matplotlib.org/stable/tutorials/toolkits/axisartist.html)\n", + "- [cartopy](https://scitools.org.uk/cartopy/docs/latest)\n", + "- [gdal](https://gdal.org/index.html)\n", + "- [wradlib xarray backends](https://docs.wradlib.org/projects/old-docs/en/1.16.0/notebooks/fileio/wradlib_xarray_backends.html)\n", + "- [rioxarray](https://corteva.github.io/rioxarray/stable/)\n", + "- [wradlib scan strategy](https://docs.wradlib.org/projects/old-docs/en/1.16.0/notebooks/visualisation/wradlib_plot_scan_strategy.html)\n", + "- [Leonardo - Rainbow5](https://electronics.leonardo.com/en/products/rainbow-5-application-software)\n", + "- [OPERA ODIM_H5](https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/)\n", + "- [WMO JET-OWR](https://community.wmo.int/governance/commission-membership/commission-observation-infrastructure-and-information-systems-infcom/commission-infrastructure-officers/infcom-management-group/standing-committee-measurements-instrumentation-and-traceability-sc-mint/joint-expert-team)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "nbdime-conflicts": { + "local_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python 3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ], + "remote_diff": [ + { + "diff": [ + { + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "Python3" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } + ], + "key": "display_name", + "op": "patch" + } + ], + "key": "kernelspec", + "op": "patch" + } + ] + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_preview/104/_sources/package-development/README.md b/_preview/104/_sources/package-development/README.md new file mode 100644 index 00000000..c8f48818 --- /dev/null +++ b/_preview/104/_sources/package-development/README.md @@ -0,0 +1,54 @@ +# Developing Open Source Software Packages + +This section covers best practises of contributing to existing software packages +and of creating new packages. + +## HowTo collaborate + +Collaboration is a wide field, not only code contribution. + +- **GitHub Discussions** + - [pyart](https://github.com/ARM-DOE/pyart/discussions) + - [xarray](https://github.com/pydata/xarray/discussions) +- **Issues** + - Bug Report eg. [wradlib](https://github.com/wradlib/wradlib/issues/549) + - Feature Requests eg. [wradlib](https://github.com/wradlib/wradlib/issues/461) +- **Pull Request/Merge Request** + + Not only code but also documentation can be added or extended. Even correcting typos is useful and appreciated by most package maintainers. + Sometimes issues are labeled (eg `easy first issue`) for easy identification. + - [wradlib](https://github.com/wradlib/wradlib/pull/550) + - [pyart](https://github.com/ARM-DOE/pyart/pull/807) +- **Mailing Lists** + - [wradlib](https://groups.google.com/g/wradlib-users) + - [xarray](https://groups.google.com/g/xarray) +- **Discourse** + - [pangeo](https://discourse.pangeo.io/) + - [matplotlib](https://discourse.matplotlib.org/) + +The main documentation of the projects are a good place to get information about collaboration. +For the projects involved here, those documentation locations can be found here: + +https://openradarscience.org/projects/ + +```{important} +It's good practice to adhere to the project's Code of Conduct. But in any case - be friendly and welcoming. +``` + +## HowTo create an own package + +```{important} +Advise: use a cookiecutter! +A cookiecutter is a command-line utility that creates projects from cookiecutters (project templates), e.g. creating a Python package project from a Python package project template. +``` + +We use the Python cookiecutter-template from https://github.com/audreyr/cookiecutter-pypackage.git but without all bells and whistles. + +Let's just create a package with some function, install and test it. + + + + + + + diff --git a/_preview/104/_sources/package-overview/README.md b/_preview/104/_sources/package-overview/README.md new file mode 100644 index 00000000..ff030428 --- /dev/null +++ b/_preview/104/_sources/package-overview/README.md @@ -0,0 +1,3 @@ +# Open Radar Community + +An overview of the open radar science community. \ No newline at end of file diff --git a/_preview/104/_sources/pyart/README.md b/_preview/104/_sources/pyart/README.md new file mode 100644 index 00000000..0e3506c9 --- /dev/null +++ b/_preview/104/_sources/pyart/README.md @@ -0,0 +1,19 @@ +# Py-ART Tutorial + +This is an overview of Py-ART, in 45 minutes! + +## Py-ART Basics and Gridding +The foundational content includes the: + +- Py-ART Basics - an overview of Py-ART package, how to read in data, and basic plotting functionality +- Py-ART Gridding - how to utilize the gridding tools in Py-ART + +If you are new to Py-ART, starting with the basics is a good place to start, and is required to know before moving onto Py-ART Gridding. + +## Py-ART Corrections and Calculations +Once learning the basics, we jump into applying filtering, corrections, and analyzing wind data, focusing on: +- Filtering and retrievals on raw Swiss C-band data +- Processing of Doppler wind data from a Swiss volumetric scan + +These notebooks also come with an exercise, where you can apply the lessons learned from the basics and workflows to complete a set of tasks! + diff --git a/_preview/104/_sources/workflows/README.md b/_preview/104/_sources/workflows/README.md new file mode 100644 index 00000000..ef79b972 --- /dev/null +++ b/_preview/104/_sources/workflows/README.md @@ -0,0 +1,3 @@ +# Combining Radar Workflows + +How to combine the different tools to complete your radar data workflow. \ No newline at end of file diff --git a/_preview/104/_sources/wradlib/README.md b/_preview/104/_sources/wradlib/README.md new file mode 100644 index 00000000..229115ea --- /dev/null +++ b/_preview/104/_sources/wradlib/README.md @@ -0,0 +1,33 @@ +# Wradlib Tutorial + +This is a short overview of wradlib capabilities in 45 minutes. Brace yourself. + +## Radar data IO, Visualisation, Gridding and GIS export + +Reading routines for several radar data formats based on xarray are shown. +Data will be read into xarray datasets with easy access for quick analysis and +visualisation. Datasets can be written to ODIM_H5 and CfRadial2-like files. +Easy multiprocessing using DASK can be facilitated. Data is gridded and +exported to Geoinformation Systems (GIS) formats. + +## Data Quality Processing and Beamblockage + +A very short overview on the available data quality algorithms with clutter +detection and beamblockage calculations. + +## Attenuation Correction using ZPHI-Method + +Already quite aged, the attenuation correction based on the ZPHI-method +(see [Testud et. al.](https://doi.org/10.1175/1520-0426(2000)017%3C0332:TRPAAT%3E2.0.CO;2)) +is still one of the most used algorithms in weather radar. A quick walk through +the necessary steps to derive specific attenuation is shown. The benefits of +specific attenuation derived KDP is shown. + +## Quasi Vertical Profiles (QVP) + +A short introduction into QVPs is given. + +## Short Overview over wradlib features + +Here we just mention the capabilities of wradlib and emphasize a few of the new +enhancements in recent wradlib versions. diff --git a/_preview/104/_static/__init__.py b/_preview/104/_static/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/_preview/104/_static/__pycache__/__init__.cpython-39.pyc b/_preview/104/_static/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..9ba76c9e Binary files /dev/null and b/_preview/104/_static/__pycache__/__init__.cpython-39.pyc differ diff --git a/_preview/104/_static/basic.css b/_preview/104/_static/basic.css new file mode 100644 index 00000000..d54be806 --- /dev/null +++ b/_preview/104/_static/basic.css @@ -0,0 +1,906 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; 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+ display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_preview/104/_static/copybutton.js b/_preview/104/_static/copybutton.js new file mode 100644 index 00000000..2ea7ff3e --- /dev/null +++ b/_preview/104/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_preview/104/_static/copybutton_funcs.js b/_preview/104/_static/copybutton_funcs.js new file mode 100644 index 00000000..dbe1aaad --- /dev/null +++ b/_preview/104/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_preview/104/_static/css/blank.css b/_preview/104/_static/css/blank.css new file mode 100644 index 00000000..8a686ec7 --- /dev/null +++ b/_preview/104/_static/css/blank.css @@ -0,0 +1,2 @@ +/* This file is intentionally left blank to override the stylesheet of the +parent theme via theme.conf. The parent style we import directly in theme.css */ \ No newline at end of file diff --git a/_preview/104/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css b/_preview/104/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css new file mode 100644 index 00000000..9b1c5d79 --- /dev/null +++ b/_preview/104/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap v4.5.0 (https://getbootstrap.com/) + * Copyright 2011-2020 The Bootstrap Authors + * Copyright 2011-2020 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + 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i.fas{vertical-align:middle;font-style:normal;font-size:1.5rem;line-height:1.25}#navbar-icon-links i.fa-github-square:before{color:#333}#navbar-icon-links i.fa-twitter-square:before{color:#55acee}#navbar-icon-links i.fa-gitlab:before{color:#548}#navbar-icon-links i.fa-bitbucket:before{color:#0052cc}.tocsection{border-left:1px solid #eee;padding:.3rem 1.5rem}.tocsection i{padding-right:.5rem}.editthispage{padding-top:2rem}.editthispage a{color:var(--pst-color-sidebar-link-active)}.xr-wrap[hidden]{display:block!important}.toctree-checkbox{position:absolute;display:none}.toctree-checkbox~ul{display:none}.toctree-checkbox~label i{transform:rotate(0deg)}.toctree-checkbox:checked~ul{display:block}.toctree-checkbox:checked~label i{transform:rotate(180deg)}.bd-sidebar li{position:relative}.bd-sidebar label{position:absolute;top:0;right:0;height:30px;width:30px;cursor:pointer;display:flex;justify-content:center;align-items:center}.bd-sidebar label:hover{background:rgba(var(--pst-color-sidebar-expander-background-hover),1)}.bd-sidebar label i{display:inline-block;font-size:.75rem;text-align:center}.bd-sidebar label i:hover{color:rgba(var(--pst-color-sidebar-link-hover),1)}.bd-sidebar li.has-children>.reference{padding-right:30px}div.doctest>div.highlight span.gp,span.linenos,table.highlighttable td.linenos{user-select:none;-webkit-user-select:text;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none}.docutils.container{padding-left:unset;padding-right:unset} \ No newline at end of file diff --git a/_preview/104/_static/css/theme.css b/_preview/104/_static/css/theme.css new file mode 100644 index 00000000..2e03fe37 --- /dev/null +++ b/_preview/104/_static/css/theme.css @@ -0,0 +1,120 @@ +/* Provided by the Sphinx base theme template at build time */ +@import "../basic.css"; + +:root { + /***************************************************************************** + * Theme config + **/ + --pst-header-height: 60px; + + /***************************************************************************** + * Font size + **/ + --pst-font-size-base: 15px; /* base font size - applied at body / html level */ + + /* heading font sizes */ + --pst-font-size-h1: 36px; + --pst-font-size-h2: 32px; + --pst-font-size-h3: 26px; + --pst-font-size-h4: 21px; + --pst-font-size-h5: 18px; + --pst-font-size-h6: 16px; + + /* smaller then heading font sizes*/ + --pst-font-size-milli: 12px; + + --pst-sidebar-font-size: .9em; + --pst-sidebar-caption-font-size: .9em; + + /***************************************************************************** + * Font family + **/ + /* These are adapted from https://systemfontstack.com/ */ + --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", + Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; + --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, + Liberation Mono, Lucida Console, monospace; + + --pst-font-family-base: var(--pst-font-family-base-system); + --pst-font-family-heading: var(--pst-font-family-base); + --pst-font-family-monospace: var(--pst-font-family-monospace-system); + + /***************************************************************************** + * Color + * + * Colors are defined in rgb string way, "red, green, blue" + **/ + --pst-color-primary: 19, 6, 84; + --pst-color-success: 40, 167, 69; + --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ + --pst-color-warning: 255, 193, 7; + --pst-color-danger: 220, 53, 69; + --pst-color-text-base: 51, 51, 51; + + --pst-color-h1: var(--pst-color-primary); + --pst-color-h2: var(--pst-color-primary); + --pst-color-h3: var(--pst-color-text-base); + --pst-color-h4: var(--pst-color-text-base); + --pst-color-h5: var(--pst-color-text-base); + --pst-color-h6: var(--pst-color-text-base); + --pst-color-paragraph: var(--pst-color-text-base); + --pst-color-link: 0, 91, 129; + --pst-color-link-hover: 227, 46, 0; + --pst-color-headerlink: 198, 15, 15; + --pst-color-headerlink-hover: 255, 255, 255; + --pst-color-preformatted-text: 34, 34, 34; + --pst-color-preformatted-background: 250, 250, 250; + --pst-color-inline-code: 232, 62, 140; + + --pst-color-active-navigation: 19, 6, 84; + --pst-color-navbar-link: 77, 77, 77; + --pst-color-navbar-link-hover: var(--pst-color-active-navigation); + --pst-color-navbar-link-active: var(--pst-color-active-navigation); + --pst-color-sidebar-link: 77, 77, 77; + --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); + --pst-color-sidebar-link-active: var(--pst-color-active-navigation); + --pst-color-sidebar-expander-background-hover: 244, 244, 244; + --pst-color-sidebar-caption: 77, 77, 77; + --pst-color-toc-link: 119, 117, 122; + --pst-color-toc-link-hover: var(--pst-color-active-navigation); + --pst-color-toc-link-active: var(--pst-color-active-navigation); + + /***************************************************************************** + * Icon + **/ + + /* font awesome icons*/ + --pst-icon-check-circle: '\f058'; + --pst-icon-info-circle: '\f05a'; + --pst-icon-exclamation-triangle: '\f071'; + --pst-icon-exclamation-circle: '\f06a'; + --pst-icon-times-circle: '\f057'; + --pst-icon-lightbulb: '\f0eb'; + + /***************************************************************************** + * Admonitions + **/ + + --pst-color-admonition-default: var(--pst-color-info); + --pst-color-admonition-note: var(--pst-color-info); + --pst-color-admonition-attention: var(--pst-color-warning); + --pst-color-admonition-caution: var(--pst-color-warning); + --pst-color-admonition-warning: var(--pst-color-warning); + --pst-color-admonition-danger: var(--pst-color-danger); + --pst-color-admonition-error: var(--pst-color-danger); + --pst-color-admonition-hint: var(--pst-color-success); + --pst-color-admonition-tip: var(--pst-color-success); + --pst-color-admonition-important: var(--pst-color-success); + + --pst-icon-admonition-default: var(--pst-icon-info-circle); + --pst-icon-admonition-note: var(--pst-icon-info-circle); + --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); + --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); + --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); + --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); + --pst-icon-admonition-error: var(--pst-icon-times-circle); + --pst-icon-admonition-hint: var(--pst-icon-lightbulb); + --pst-icon-admonition-tip: var(--pst-icon-lightbulb); + --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); + +} diff --git a/_preview/104/_static/doctools.js b/_preview/104/_static/doctools.js new file mode 100644 index 00000000..e1bfd708 --- /dev/null +++ b/_preview/104/_static/doctools.js @@ -0,0 +1,358 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for all documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + this.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + var url = new URL(window.location); + url.searchParams.delete('highlight'); + window.history.replaceState({}, '', url); + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar : function() { + $('input[name=q]').first().focus(); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + return; + + $(document).keydown(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box, textarea, dropdown or button + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' + && activeElementType !== 'BUTTON') { + if (event.altKey || event.ctrlKey || event.metaKey) + return; + + if (!event.shiftKey) { + switch (event.key) { + case 'ArrowLeft': + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) + break; + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + break; + case 'ArrowRight': + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) + break; + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + break; + case 'Escape': + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + break; + Documentation.hideSearchWords(); + return false; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case '/': + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + break; + Documentation.focusSearchBar(); + return false; + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { + Documentation.init(); +}); diff --git a/_preview/104/_static/documentation_options.js b/_preview/104/_static/documentation_options.js new file mode 100644 index 00000000..877e3c31 --- /dev/null +++ b/_preview/104/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '', + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_preview/104/_static/erad_white_background.png b/_preview/104/_static/erad_white_background.png new file mode 100644 index 00000000..4804302d Binary files /dev/null and b/_preview/104/_static/erad_white_background.png differ diff --git a/_preview/104/_static/file.png b/_preview/104/_static/file.png new file mode 100644 index 00000000..a858a410 Binary files /dev/null and b/_preview/104/_static/file.png differ diff --git a/_preview/104/_static/images/logo_binder.svg b/_preview/104/_static/images/logo_binder.svg new file mode 100644 index 00000000..45fecf75 --- /dev/null +++ b/_preview/104/_static/images/logo_binder.svg @@ -0,0 +1,19 @@ + + + + +logo + + + + + + + + diff --git a/_preview/104/_static/images/logo_colab.png b/_preview/104/_static/images/logo_colab.png new file mode 100644 index 00000000..b7560ec2 Binary files /dev/null and b/_preview/104/_static/images/logo_colab.png differ diff --git a/_preview/104/_static/images/logo_jupyterhub.svg b/_preview/104/_static/images/logo_jupyterhub.svg new file mode 100644 index 00000000..60cfe9f2 --- /dev/null +++ b/_preview/104/_static/images/logo_jupyterhub.svg @@ -0,0 +1 @@ +logo_jupyterhubHub diff --git a/_preview/104/_static/jquery-3.5.1.js b/_preview/104/_static/jquery-3.5.1.js new file mode 100644 index 00000000..50937333 --- /dev/null +++ b/_preview/104/_static/jquery-3.5.1.js @@ -0,0 +1,10872 @@ +/*! + * jQuery JavaScript Library v3.5.1 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2020-05-04T22:49Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + return typeof obj === "function" && typeof obj.nodeType !== "number"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.5.1", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.5 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2020-03-14 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem.namespaceURI, + docElem = ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +}; +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the master Deferred + master = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + master.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( master.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return master.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + } + + return master.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, + rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + return result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + + which: function( event ) { + var button = event.button; + + // Add which for key events + if ( event.which == null && rkeyEvent.test( event.type ) ) { + return event.charCode != null ? event.charCode : event.keyCode; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { + if ( button & 1 ) { + return 1; + } + + if ( button & 2 ) { + return 3; + } + + if ( button & 4 ) { + return 2; + } + + return 0; + } + + return event.which; + } +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px"; + tr.style.height = "1px"; + trChild.style.height = "9px"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( + dataPriv.get( cur, "events" ) || Object.create( null ) + )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) { + xml = undefined; + } + + if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ) + .filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ) + .map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script + if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " +{% endmacro %} \ No newline at end of file diff --git a/_preview/104/baltrad/README.html b/_preview/104/baltrad/README.html new file mode 100644 index 00000000..d3b4283f --- /dev/null +++ b/_preview/104/baltrad/README.html @@ -0,0 +1,571 @@ + + + + + + + + BALTRAD Tutorial — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
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+ + + + + + +
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+ +
+ + + +
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+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

BALTRAD Tutorial

+

The BALTRAD approach will be introduced by outlining the ways in which it is commonly deployed, and then the data representation model used by the software will be explained.

+

Following this introduction, course participants will get the chance to aquaint themselves with the look and feel of the BALTRAD Toolbox by running through a few notebooks that address data representation, quality control, compositing, and other features. Interoperability, how two software packages can pass data to each other, will be demonstrated by chosing between notebooks that do this between BALTRAD and Py-ART or between BALTRAD and wradlib.

+
+
+
+ + + + +
+ +
+ + +
+

previous

+

wradlib time series data and quasi vertical profiles

+
+ + +
+

next

+

Compositing with BALTRAD

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/genindex.html b/_preview/104/genindex.html new file mode 100644 index 00000000..d3bb6a4b --- /dev/null +++ b/_preview/104/genindex.html @@ -0,0 +1,516 @@ + + + + + + + + Index — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
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+ + + + + + +
+ +
+ + + +
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+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ + +

Index

+ +
+ +
+ + +
+ +
+
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/index.html b/_preview/104/index.html new file mode 100644 index 00000000..960bb69b --- /dev/null +++ b/_preview/104/index.html @@ -0,0 +1 @@ + diff --git a/_preview/104/introductions/getting-started.html b/_preview/104/introductions/getting-started.html new file mode 100644 index 00000000..f1c2f6b4 --- /dev/null +++ b/_preview/104/introductions/getting-started.html @@ -0,0 +1,762 @@ + + + + + + + + Getting Started — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
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+ + + + + +
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+ + + + +
+ + +
+ + + + + + +
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+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

Getting Started

+
+

About

+

The repository was created from a ProjectPythia cookbook template. +This template brings with it all machinery to enable full featured GitHub workflows, including building a docker image, running and rendering +Jupyter Notebooks and compiling a website using Sphinx and the JupyterBook theme.

+
+
+

Customizing

+

If there is a package missing for your use-case you can just add the package to binder/environment.yml and activate a new build (see below). +Notebooks might be added to the notebooks-folder.

+
+
+

Build workflow

+

We work on pre-building the environment and toolkit so you can deploy this on the platform of your choice! Making the Open Radar Science stack available across different environments is essential to ensure reproducibility. We take advantage of “containerization”, which enables packaging all of these tools together so you can deploy it on the platform of your choice. In this case, we are deploying a software stack which includes a variety of different languages (ex. Python, C++, C) and deploy it on a cloud platform. We use Docker as our main containerization tool.

+
+

Build on GitHub - PullRequest

+
    +

  1. repo2docker-action is used to build the docker image

    +
      +

    • docker layers will be cached from ghcr.io

      • +

    • image is not pushed, since GITHUB_TOKEN write is not available as per security policy

      • +
    +
    2. +

  2. book is built in a second step using docker-run-action, zipped and uploaded as artifact

    3. +

  3. book is deployed to gh-pages, link is added to pr comment

    4. +
+
+
+

Build on GitHub - Push

+
    +

  1. repo2docker-action is used to build the docker image

    +
      +

    • docker layers will be cached from ghcr.io

      • +

    • image is pushed to ghcr.io

      • +
    +
    2. +

  2. book is built in a second job directly inside ghcr.io, zipped and uploaded as artifact

    3. +

  3. book is deployed to gh-pages

    4. +
+

Depending on the changes in the repo the build times can be quite low as docker layer caching is facilitated.

+
+
+

Build & run locally

+

If you want to build and run locally using repo2docker you would need to remove (temporarily) the binder/Dockerfile. Then you would need to invoke the build with:

+
$ repo2docker --appendix "`cat binder/appendix.txt`" .
+
+
+

This will build the docker image locally and fire up a container running jupyterlab.

+
+
+

Run locally

+

If you want to just run locally using repo2docker you will just use the provided binder/Dockerfile. Then you would need to invoke the run with:

+
$ repo2docker .
+
+
+

This will fetch the docker image from ghcr and fire up a container running jupyterlab.

+
+
+

Health check

+

The complete build workflow is run as nightly build in GitHub CI to early detect problems. The Github action builds the environment, executes the notebooks, and checks the different links within the content. This ensures that the content is still executable, and we do not run into issues when building the environment or running the computational workflows.

+

Running these health checks results in the following badge: +nightly-build

+

Which is included in our README, letting users know whether the content is still executable. If this workflow fails, it also notifies the developers of this repository that there is an issue.

+
+
+

Conclusion

+
    +

  • Docker images are built using GHA with all essential components inside (and pushed to ghcr.io on push)

    • +

  • On binder we are using now our prebuild images on ghcr.io. As we are using appendix instead of postBuild no compilations are necessary on the binder side, the images is used as is with some minor adaptions by binder.

    • +

  • On GHA we are using our prebuild images as layer cache, but layers can’t be updated on pull requests due to security reasons.

    • +

  • The erad2022 package on ghcr.io represents always the status of the most recent commit to the repo.

    • +

  • Running locally as well as building & running is easily possible.

    • +
+
+
+
+

Using the PANGEO Cloud with Binder

+
+

1. Make Sure You Have a Github Account

+

The first step is to make sure you have a Github Account.

+

Here is the link if you do have one already:

+ +
+
+

2. Log into Pangeo Binder

+

Next, sign in and authenticate the Pangeo Binder, which is the platform we will use for the workshop:

+ +

The JupyterHub instance we use for this course is relatively small in memory and compute power (~10 GB of memory, 2 CPU cores). For more information about all of the open computational resrouces available within the Pangeo community, check out the Pangeo Cloud documentation.

+
+
+

3. Launch our Environment

+

Now that we have our authentication set up, we can access our content!

+

Use the following link to launch into the binder:

+ +

If you are having issues with that (ex. it is taking a long time), try using the following link:

+
https://hub.aws-uswest2-binder.pangeo.io/user/{your_github_username}/lab
+
+
+

Where you replace {your_github_username} with your github username (ex. mgrover1)

+
+
+
+

Running notebooks

+

If you finally succeeded with the above procedure you will have a JupyterLab instance up and running. +Then you can select the notebooks as usual from the right navigation and simply run through them. +If you encounter any problems please raise an issue here.

+
+
+ + + + +
+ +
+ + +
+

previous

+

ERAD 2022 Open Radar Science Shortcourse

+
+ + +
+

next

+

An Overview of Open Science

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/introductions/open-radar.html b/_preview/104/introductions/open-radar.html new file mode 100644 index 00000000..ed8d01e1 --- /dev/null +++ b/_preview/104/introductions/open-radar.html @@ -0,0 +1,568 @@ + + + + + + + + Open Radar Community — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

Open Radar Community

+

An overview of the open radar science community.

+
+ + + + +
+ +
+ + +
+

previous

+

An Overview of Open Science

+
+ + +
+

next

+

Py-ART Tutorial

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/introductions/open-science.html b/_preview/104/introductions/open-science.html new file mode 100644 index 00000000..b98a2325 --- /dev/null +++ b/_preview/104/introductions/open-science.html @@ -0,0 +1,568 @@ + + + + + + + + An Overview of Open Science — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+
+
+ +
+ +
+

An Overview of Open Science

+

Open science introduction.

+
+ + + + +
+ +
+ + +
+

previous

+

Getting Started

+
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+

next

+

Open Radar Community

+
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+ Meteoswiss +
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+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/lrose/README.html b/_preview/104/lrose/README.html new file mode 100644 index 00000000..d4d813f5 --- /dev/null +++ b/_preview/104/lrose/README.html @@ -0,0 +1,570 @@ + + + + + + + + LROSE Tutorial — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

LROSE Tutorial

+

An overview and introduction to LROSE.

+
+
+
+ + + + +
+ +
+ + +
+

previous

+

BALTRAD Quality Control

+
+ + +
+

next

+

ERAD 2022 Open Source Workshop

+
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+ Canada +
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+ NCAR +
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+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad2wradlib/README.html b/_preview/104/notebooks/baltrad2wradlib/README.html new file mode 100644 index 00000000..29f42659 --- /dev/null +++ b/_preview/104/notebooks/baltrad2wradlib/README.html @@ -0,0 +1,553 @@ + + + + + + + + baltrad2wradlib — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

baltrad2wradlib

+

This is the interoperability demonstration between BALTRAD and wradlib.

+

It contains the input ODIM_H5 file in folder /in and a notebook for the exercise.

+
+ + + + +
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+ Bonne +
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+ Canada +
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+ NCAR +
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+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad2wradlib/baltrad2wradlib.html b/_preview/104/notebooks/baltrad2wradlib/baltrad2wradlib.html new file mode 100644 index 00000000..d3cf0650 --- /dev/null +++ b/_preview/104/notebooks/baltrad2wradlib/baltrad2wradlib.html @@ -0,0 +1,1026 @@ + + + + + + + + Interaction of BALTRAD and wradlib via ODIM_H5 — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
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+ +
+ On this page +
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+ +
+ +
+

Interaction of BALTRAD and wradlib via ODIM_H5

+
+

Prepare your environment

+
+
+
%matplotlib inline
+
+
+
+
+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+from matplotlib.collections import PatchCollection
+import matplotlib.ticker as mticker
+import wradlib
+import shapefile
+import gc
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

Run BALTRAD’s odc_toolbox

+

First, you will process a scan from Suergavere (Estland) by using BALTRAD’s odc_toolbox.

+

From your VM’s vagrant directory, navigate to the folder /baltrad2wradlib.

+

Execute the following command:

+

$ odc_toolbox -i in -o out -q ropo,radvol-att

+

Check whether a file was created in the folder /out.

+

BALTRAD will not create output files if these already exist. You can check that via !ls out.

+
+
+
!odc_toolbox -i in -o out -q ropo,radvol-att
+
+
+
+
+
Exception ignored in: <function _after_at_fork_child_reinit_locks at 0x7f551340a820>
+Traceback (most recent call last):
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 255, in _after_at_fork_child_reinit_locks
+    handler._at_fork_reinit()
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 894, in _at_fork_reinit
+    self.lock._at_fork_reinit()
+AttributeError: 'RLock' object has no attribute '_at_fork_reinit'
+Exception ignored in: <function _after_at_fork_child_reinit_locks at 0x7f551340a820>
+Traceback (most recent call last):
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 255, in _after_at_fork_child_reinit_locks
+    handler._at_fork_reinit()
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 894, in _at_fork_reinit
+    self.lock._at_fork_reinit()
+AttributeError: 'RLock' object has no attribute '_at_fork_reinit'
+Exception ignored in: <function _after_at_fork_child_reinit_locks at 0x7f551340a820>
+Traceback (most recent call last):
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 255, in _after_at_fork_child_reinit_locks
+    handler._at_fork_reinit()
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 894, in _at_fork_reinit
+    self.lock._at_fork_reinit()
+AttributeError: 'RLock' object has no attribute '_at_fork_reinit'
+Exception ignored in: <function _after_at_fork_child_reinit_locks at 0x7f551340a820>
+Traceback (most recent call last):
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 255, in _after_at_fork_child_reinit_locks
+    handler._at_fork_reinit()
+  File "/srv/conda/envs/notebook/lib/python3.9/logging/__init__.py", line 894, in _at_fork_reinit
+    self.lock._at_fork_reinit()
+AttributeError: 'RLock' object has no attribute '_at_fork_reinit'
+
+
+
Objects created: 1509
+Objects deleted: 1509
+Objects pending: 0
+
+
+
+
+
+
+
!ls out
+
+
+
+
+
201405190715_SUR.h5
+
+
+
+
+
+
+

Read and inspect data from Suergavere (Estonia) before and after QC with odc_toolbox

+
+
+
# Before QC
+inp = wradlib.io.read_opera_hdf5("in/201405190715_SUR.h5")
+# After QC
+out = wradlib.io.read_opera_hdf5("out/201405190715_SUR.h5")
+
+
+
+
+
+
+
# Here you can inspect whichever directory you want, e.g.
+print("where...\n", inp['where'],"\n")
+print("what...\n", inp['what'],"\n")
+print("dataset1/data1/what...\n", inp['dataset1/data1/what'])
+
+
+
+
+
where...
+ {'height': 157.0, 'lat': 58.482310026884086, 'lon': 25.518660116940737, 'towerheight': 29.0} 
+
+what...
+ {'date': b'20140519', 'object': b'PVOL', 'source': b'WMO:26232,RAD:EE41,PLC:S\xc3\xbcrgavere,NOD:eesur', 'time': b'071500', 'version': b'H5rad 2.2'} 
+
+dataset1/data1/what...
+ {'gain': 0.5, 'nodata': 255.0, 'offset': -32.0, 'quantity': b'DBZH', 'undetect': 0.0}
+
+
+
+
+
+
+

Convert selected fields to target units

+
+
+
def convert(dset, dir):
+    """Converts ODIM_H5 data representations to values in target unit and masks missing values.
+    
+    Parameters
+    ----------
+    dset : the hdf5 object returned by wradlib.read_OPERA_hdf5
+    dir : the corresponding directory tree in dset for which the data should be processed
+    
+    """
+    res = dset[dir+"/data"].astype(np.uint16)
+    try:
+        res = res * dset[dir+"/what"]["gain"] + dset[dir+"/what"]["offset"]
+    except:
+        pass
+    try:
+        res[dset[dir+"/data"]==dset[dir+"/what"]["nodata"]] = np.nan
+    except:
+        pass
+    try:
+        res[dset[dir+"/data"]==dset[dir+"/what"]["undetect"]] = np.nan
+    except:
+        pass    
+
+    return res   
+
+
+
+
+
+
+
# Extract specific data arrays from ODIM_h5 objects
+#    dBZH before QC
+before= convert(inp, "dataset1/data1")
+#    dBZH after QC
+after = convert(out, "dataset1/data1")
+#    Quality field 1
+qual1 = convert(out, "dataset1/quality1")
+#    Quality field 2
+qual2 = convert(out, "dataset1/quality2")
+
+
+
+
+
+
+

Collect all the information required for georeferencing

+
+
+
# Collect all the georeferencing information we need
+#   First gate
+r0 = inp['dataset1/where']["rstart"]*1000.
+#   Gate length
+rscale = inp['dataset1/where']["rscale"]
+#   Number of bins per beam
+nbins = inp['dataset1/where']["nbins"]
+#   Maximum range
+maxr = r0 + (nbins)*rscale
+# Construct array of range gates
+r = np.linspace(r0, maxr, nbins)
+# Construct array of azimuth angles (quick and dirty)
+az = np.linspace(0, 359, inp['dataset1/where']["nrays"])
+# Site coordinates (lon,lat)
+site = (inp["where"]["lon"], inp["where"]["lat"], inp["where"]["height"])
+# Define a projection (Azimuthal Equidistant)
+proj = wradlib.georef.create_osr("aeqd", lon_0=site[0], lat_0=site[1])
+
+
+
+
+
+
+

Design a plot we will use for all PPIs in this exercise

+
+
+
# A little helper funciton to harmonize all plots
+def plot_ppi_to_ax(ppi, ax, title="", cb=True, cb_label="", cb_shrink=0.75, bbox=[-maxr, maxr, -maxr, maxr], extend="min", **kwargs):
+    """This is the function that we use in this exercise to plot PPIs with uniform georeferencing and style.
+    """
+    # Read, project and plot country shapefile as background
+    # (got that snippet from http://stackoverflow.com/questions/15968762/shapefile-and-matplotlib-plot-polygon-collection-of-shapefile-coordinates)
+    sf = shapefile.Reader("shp/europe_countries.shp", encoding="latin1")
+    recs    = sf.records()
+    shapes  = sf.shapes()
+    Nshp    = len(shapes)
+    # Iterate over shapes and create matplotlib Polygons
+    for nshp in range(Nshp):
+        ptchs   = []
+        pts     = np.array(shapes[nshp].points)
+        pts = wradlib.georef.reproject(pts, projection_target=proj)
+        prt     = shapes[nshp].parts
+        par     = list(prt) + [pts.shape[0]]
+        for pij in range(len(prt)):
+            ptchs.append(Polygon(pts[par[pij]:par[pij+1]]))
+        ax.add_collection(PatchCollection(ptchs,facecolor="lightgrey",edgecolor='k', linewidths=1, zorder=-1))
+
+
+    # use wradlib.vis.plot_ppi
+    ax, pm = wradlib.vis.plot_ppi(np.ma.masked_invalid(ppi), ax=ax, r=r, az=az, site=site, proj=proj, **kwargs)
+    # and plot a cross hair
+    ax = wradlib.vis.plot_ppi_crosshair(site=site, ranges=[50000, 100000,150000, 200000, maxr], angles=[0, 90, 180, 270], 
+                                        proj=proj, elev=0.5, ax=ax)
+    ##plt.text(x=-244000, y=-60000,s="250km", color="gray", rotation=-69)
+    ##plt.text(x=-192000, y=-60000,s="200km", color="gray", rotation=-67)
+    ##plt.text(x=-138000, y=-60000,s="150km", color="gray", rotation=-65)
+    
+      
+    # Do our final decorations
+    plt.xlim(bbox[0], bbox[1])
+    plt.ylim(bbox[2], bbox[3])
+    # set title
+    plt.title(title)
+    # convert axes unit from meters to kilometers
+    xticks_loc = ax.get_xticks().tolist()
+    yticks_loc = ax.get_yticks().tolist()
+    ax.xaxis.set_major_locator(mticker.FixedLocator(xticks_loc))
+    ax.yaxis.set_major_locator(mticker.FixedLocator(yticks_loc))
+    ax.set_xticklabels(['%g' % (0.001*l) for l in ax.get_xticks()])
+    ax.set_yticklabels(['%g' % (0.001*l) for l in ax.get_yticks()])
+    # set axes lables
+    plt.xlabel("x coordinates (km)", fontsize="large")
+    plt.ylabel("y coordinates (km)", fontsize="large")
+    # plot colorbar   
+    if cb:
+        cbar = plt.colorbar(pm, shrink=cb_shrink, orientation="horizontal", extend=extend, pad=0.1)
+        cbar.set_label(cb_label, fontsize="large")
+    
+    gc.collect()
+    
+    return ax, pm
+    
+
+
+
+
+
+
+

Plot the selected fields into one figure

+
+
+
fig = plt.figure(figsize=(12,10))
+
+ax = plt.subplot(221, aspect="equal")
+ax, pm = plot_ppi_to_ax(before, ax=ax, title="Before QC", cb=False, vmin=-10, vmax=65)
+
+ax = plt.subplot(222, aspect="equal")
+ax, pm = plot_ppi_to_ax(after,  ax=ax, title="After QC",  cb=False, vmin=-10, vmax=65)
+
+ax = plt.subplot(223, aspect="equal")
+ax, qm = plot_ppi_to_ax(qual1,  ax=ax, title="Quality 1", cb=False)
+
+ax = plt.subplot(224, aspect="equal")
+ax, qm = plot_ppi_to_ax(qual2,  ax=ax, title="Quality 2", cb=False)
+
+plt.tight_layout()
+
+# Add colorbars
+fig.subplots_adjust(right=0.9)
+cax = fig.add_axes((0.9, 0.6, 0.03, 0.3))
+cbar = plt.colorbar(pm, cax=cax)
+cbar.set_label("Horizontal reflectivity (dBZ)", fontsize="large")
+
+cax = fig.add_axes((0.9, 0.1, 0.03, 0.3))
+cbar = plt.colorbar(qm, cax=cax)
+cbar.set_label("Quality index", fontsize="large")
+
+
+
+
+../../_images/baltrad2wradlib_19_0.png +
+
+
+
+

Collect and plot the polarimetric moments from the original ODIM_H5 dataset

+
+
+
# We organise the moments as a dictionary
+moments = {}
+moments["rho"] = convert(inp, "dataset1/data2")     # RhoHV
+moments["phi"] = convert(inp, "dataset1/data4")     # PhiDP
+moments["zdr"] = convert(inp, "dataset1/data5")     # ZDR - the value range is not plausible, is it? What went wrong?
+moments["dop"] = convert(inp, "dataset1/data10")    # Doppler velocity
+
+
+
+
+
+
+
fig = plt.figure(figsize=(12,12))
+
+ax = plt.subplot(221, aspect="equal")
+ax, pm = plot_ppi_to_ax(moments["rho"], ax=ax, title="RhoHV", cb_label="(-)", cb_shrink=0.6, extend="neither")
+
+ax = plt.subplot(222, aspect="equal")
+ax, pm = plot_ppi_to_ax(moments["phi"], ax=ax, title="PhiDP", cb_label="degree", cb_shrink=0.6, extend="neither")
+
+ax = plt.subplot(223, aspect="equal")
+ax, pm = plot_ppi_to_ax(moments["zdr"], ax=ax, title="Differential reflectivity", cb_label="dB", cb_shrink=0.6, extend="neither")
+
+ax = plt.subplot(224, aspect="equal")
+ax, pm = plot_ppi_to_ax(moments["dop"], ax=ax, title="Doppler velocity", cb_label="m/s", cb_shrink=0.6, extend="neither")
+
+plt.tight_layout()
+
+
+
+
+../../_images/baltrad2wradlib_22_0.png +
+
+
+
+

Try some filtering and attenuation correction

+
+
+
# Set ZH to a very low value where we do not expect valid data
+zh_filtered = np.where(np.isnan(before), -32., before)
+# Retrieve PIA by using some constraints (see http://wradlib.bitbucket.org/atten.html for help)
+pia = wradlib.atten.correct_attenuation_constrained(zh_filtered,
+                                                  constraints=[wradlib.atten.constraint_dbz,
+                                                               wradlib.atten.constraint_pia],
+                                                  constraint_args=[[64.0],
+                                                                   [20.0]])
+# Correct reflectivity by PIA
+after2 = before + pia
+# Mask out non-meteorological echoes
+after2[np.isnan(before)] = np.nan
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/wradlib/trafo.py:261: RuntimeWarning: overflow encountered in power
+  return 10.0 ** (x / 10.0)
+
+
+
+
+
+
+

Compare results against QC from odc_toolbox

+
+
+
fig = plt.figure(figsize=(18,10))
+bbox = [-maxr-2000,-100000, -100000, 100000]
+shrink = 0.8
+
+ax = plt.subplot(131, aspect="equal")
+ax, pm = plot_ppi_to_ax(before, ax=ax, title="Before QC", cb_label="Horizontal reflectivity (dBZ)", 
+                        cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)
+
+
+ax = plt.subplot(132, aspect="equal")
+ax, pm = plot_ppi_to_ax(after,  ax=ax, title="After QC using BALTRAD Toolbox", cb_label="Horizontal reflectivity (dBZ)", 
+                        cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)
+
+
+ax = plt.subplot(133, aspect="equal")
+ax, pm = plot_ppi_to_ax(after2, ax=ax, title="After QC using wradlib", cb_label="Horizontal reflectivity (dBZ)", 
+                        cb_shrink=shrink, bbox=bbox, vmin=0, vmax=65)
+
+
+
+
+../../_images/baltrad2wradlib_26_0.png +
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

Combining Radar Workflows

+
+ + +
+

next

+

In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART.

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/BALTRAD Compositing.html b/_preview/104/notebooks/baltrad_short_course/BALTRAD Compositing.html new file mode 100644 index 00000000..3c33b811 --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/BALTRAD Compositing.html @@ -0,0 +1,775 @@ + + + + + + + + Compositing with BALTRAD — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
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+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

Compositing with BALTRAD

+

This exercise builds on output from the parallel processing exercise. It does not address how projections and navigation is dealt with in BALTRAD. This should be addressed in a separate exercise.

+

The Cartesian product area used in this exercise is pre-configured and looked up from a registry.

+
+

Rudimentary composite

+
+
+
%matplotlib inline
+import glob, time
+import matplotlib
+import _raveio, _rave
+import _pycomposite, compositing
+import warnings
+warnings.filterwarnings('ignore')  # Suppress SyntaxWarning from Python2 code
+
+generator = compositing.compositing()
+generator.filenames = glob.glob("data/se*.h5")
+
+
+
+
+
+
+
# Run with all defaults to a pre-configured area that uses the Google Maps projection.
+# First two arguments are product date and time. These are taken from the last input file if not specified.
+before = time.time()
+comp = generator.generate(None, None, area="swegmaps_2000")
+after = time.time()
+
+rio = _raveio.new()
+rio.object = comp
+rio.save("data/comp_pcappi1000m.h5")
+
+print("Compositing took %3.2f seconds" % (after-before))
+
+
+
+
+
Compositing took 12.68 seconds
+
+
+
+
+
+
+

Tweak the plotter from earlier exercises

+
+
+
# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE
+from GmapColorMap import dbzh as dbzp
+
+# Convert a 768-list palette to a matplotlib colorlist
+def make_colorlist(pal):
+    colorlist = []
+    for i in range(0, len(pal), 3):
+        colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])
+    return colorlist
+
+# Convert lists to colormaps
+dbzcl = make_colorlist(dbzp)
+
+# Then create a simple plotter
+import matplotlib.pyplot as plt
+StringType = type('')
+def plot(data, colorlist=dbzcl, title="Composite"):
+    mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))
+    figsize=(20,16)# if mini == 0 else (12,8)
+    fig = plt.figure(figsize=figsize)
+    plt.title(title)
+    clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)
+    plt.imshow(data, cmap=clist, clim=(0,255))
+    plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])
+
+
+
+
+
+
+
plot(comp.getParameter("DBZH").getData(), title="Default composite: DBZH 1000 m Pseudo-CAPPI, nearest radar")
+
+
+
+
+../../_images/BALTRAD Compositing_7_0.png +
+
+
+
+

Maximum reflectivity, lowest pixel, add QC chain

+
+
+
generator.product = _rave.Rave_ProductType_MAX
+generator.selection_method = _pycomposite.SelectionMethod_HEIGHT
+generator.detectors = ["ropo", "beamb", "radvol-att", "radvol-broad", "rave-overshooting", "qi-total"]
+before = time.time()
+comp = generator.generate(None, None, area="swegmaps_2000")
+after = time.time()
+rio.object = comp
+rio.save("data/comp_max.h5")
+print("Compositing took %3.2f seconds" % (after-before))
+
+
+
+
+
Compositing took 76.22 seconds
+
+
+
+
+
+
+
plot(comp.getParameter("DBZH").getData(), title="Maximum reflectivity, lowest pixel")
+
+
+
+
+../../_images/BALTRAD Compositing_10_0.png +
+
+
+
+

Plot correspondong total quality index

+
+
+
dbzh = comp.getParameter("DBZH")
+qitot = dbzh.getQualityFieldByHowTask("pl.imgw.quality.qi_total")
+plot(qitot.getData(), "binary", "Total quality index")
+
+
+
+
+../../_images/BALTRAD Compositing_12_0.png +
+
+
+
+

Now use “total quality” as the compositing criterion

+
+
+
generator.qitotal_field = "pl.imgw.quality.qi_total"
+before = time.time()
+comp = generator.generate(None, None, area="swegmaps_2000")
+after = time.time()
+rio.object = comp
+rio.save("data/comp_qitotal.h5")
+print("Compositing took %3.2f seconds" % (after-before))
+
+
+
+
+
Compositing took 27.24 seconds
+
+
+
+
+
+
+
plot(comp.getParameter("DBZH").getData(), title="Maximum reflectivity, quality-based")
+
+
+
+
+../../_images/BALTRAD Compositing_15_0.png +
+
+
+
+
plot(comp.getParameter("DBZH").getQualityFieldByHowTask("pl.imgw.quality.qi_total").getData(), "binary", "Total quality index")
+
+
+
+
+../../_images/BALTRAD Compositing_16_0.png +
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

BALTRAD Tutorial

+
+ + +
+

next

+

In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/BALTRAD DRQC.html b/_preview/104/notebooks/baltrad_short_course/BALTRAD DRQC.html new file mode 100644 index 00000000..8cd347d4 --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/BALTRAD DRQC.html @@ -0,0 +1,971 @@ + + + + + + + + In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan

+
+

We will also visualize the data using some openly-available colour tables.

+
+
+

This notebook was originally prepared using material subsequently published in https://doi.org/10.1002/met.1929

+
+
+
%matplotlib inline
+import _raveio
+import ropo_realtime, ec_drqc
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import GmapColorMap
+
+
+
+
+
+

Block of look-ups for display

+
+
+
PALETTE = {} # To be populated
+
+UNDETECT = {"TH":GmapColorMap.PUREWHITE,
+            "DBZH":GmapColorMap.PUREWHITE,
+            "DR":GmapColorMap.PUREWHITE,
+            "VRADH":GmapColorMap.GREY5,
+            "RHOHV":GmapColorMap.PUREWHITE,
+            "ZDR":GmapColorMap.PUREWHITE}
+
+NODATA = {"TH":GmapColorMap.WEBSAFEGREY,
+          "DBZH":GmapColorMap.WEBSAFEGREY,
+          "DR":GmapColorMap.WEBSAFEGREY,
+          "VRADH":GmapColorMap.GREY8,
+          "RHOHV":GmapColorMap.WEBSAFEGREY,
+          "ZDR":GmapColorMap.WEBSAFEGREY}
+
+LEGEND = {"TH":'Radar reflectivity factor (dBZ)',
+          "DBZH":'Radar reflectivity factor (dBZ)',
+          "DR":'Depolarization ratio (dB)',
+          "VRADH":'Radial wind velocity away from radar (m/s)',
+          "RHOHV":'Cross-polar correlation coefficient',
+          "ZDR":"Differential reflectivity (dB)"}
+
+TICKS = {"TH":range(-30,80,10),
+         "DBZH":range(-30,80,10),
+         "ZDR":range(-8,9,2),
+         "RHOHV":np.arange(0,11,1)/10.,
+         "VRADH":range(-48,56,8),
+         "DR":range(-36,3,3)}
+
+
+
+
+
+
+

Colormap loader and loads

+
+
+
def loadPal(fstr, reverse=True):
+    fd = open(fstr)
+    LINES = fd.readlines()
+    fd.close()
+    pal = []
+    for line in LINES:
+        s = line.split()
+        if reverse: s.reverse()
+        for val in s:
+            pal.append(int(float(val)*255))
+    if reverse: pal.reverse()
+    return pal
+
+# Colour maps by Fabio Crameri, http://www.fabiocrameri.ch/colourmaps.php, a couple of them modified
+PALETTE["DBZH"] = loadPal("data/hawaii.txt")
+PALETTE["DR"] = loadPal("data/moleron.txt", False)  # Modified oleron
+PALETTE["ZDR"] = loadPal("data/oleron.txt", False)
+PALETTE["RHOHV"] = loadPal("data/mroma.txt")        # Modified roma
+PALETTE["VRADH"] = loadPal("data/vik.txt", False)
+
+
+
+
+
+
+

Set up the display

+
+
+
def display(obj):
+    fig = plt.figure()
+    default_size = fig.get_size_inches()
+    fig.set_size_inches((default_size[0]*2, default_size[1]*2))
+
+    paramname = obj.getParameterNames()[0]
+    pal = PALETTE[paramname]
+    pal[0], pal[1], pal[2] = UNDETECT[paramname]     # Special value - areas radiated but void of echo
+    pal[767], pal[766], pal[765] = NODATA[paramname] # Special value - areas unradiated
+    if paramname == "VRADH":
+        pal[379], pal[380], pal[381] = GmapColorMap.PUREWHITE  # VRADH isodop
+        pal[382], pal[383], pal[384] = GmapColorMap.PUREWHITE  # VRADH isodop
+        pal[385], pal[386], pal[387] = GmapColorMap.PUREWHITE  # VRADH isodop
+    colorlist = []
+    for i in range(0, len(pal), 3):
+        colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])
+
+    param = obj.getParameter(paramname)
+    data = param.getData()
+    data = data*param.gain + param.offset
+        
+    im = plt.imshow(data,cmap=matplotlib.colors.ListedColormap(colorlist))
+    cax = plt.gca()
+    cax.axes.get_xaxis().set_visible(False)
+    cax.axes.get_yaxis().set_visible(False)
+
+    cb = plt.colorbar(ticks=TICKS[paramname])
+    cb.set_label(LEGEND[paramname])
+ 
+    plt.show()
+
+
+
+
+
+
+
+

Do the science

+
+

Read the polar volume, QC the reflectivity using legacy ROPO, and then save the QC:ed result

+
+
+
rio = _raveio.open('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.16.h5')
+rio.object = ropo_realtime.generate(rio.object)
+rio.save('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.ropo.h5')
+
+
+
+
+
+
+

Re-read the polar volume, QC it using depolarization ratio, and then save the QC:ed result

+
+
+
rio = _raveio.open('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.16.h5')
+pvol = rio.object
+ec_drqc.drQC(pvol)
+rio.object = pvol
+rio.save('data/2019051509_00_ODIMH5_PVOL6S_VOL_casra.drqc.h5')
+
+
+
+
+
+
+
+

NOW LEAVE THIS NOTEBOOK AND GO TO A TERMINAL to generate CAPPIs

+
+

Read and display CAPPIs, starting with Doppler-corrected reflectivity

+
+
+
cappi = _raveio.open('data/cappi_DBZH.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [7], in <module>
+----> 1 cappi = _raveio.open('data/cappi_DBZH.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

Differential reflectivity

+
+
+
cappi = _raveio.open('data/cappi_ZDR.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [8], in <module>
+----> 1 cappi = _raveio.open('data/cappi_ZDR.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

Cross-polar correlation coefficient

+
+
+
cappi = _raveio.open('data/cappi_RHOHV.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [9], in <module>
+----> 1 cappi = _raveio.open('data/cappi_RHOHV.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

Radial wind velocity, lowest PPI

+
+
+
ppi = _raveio.open('data/ppi_VRADH.h5').object
+display(ppi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [10], in <module>
+----> 1 ppi = _raveio.open('data/ppi_VRADH.h5').object
+      2 display(ppi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

Depolarization ratio

+
+
+
cappi = _raveio.open('data/cappi_DR.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [11], in <module>
+----> 1 cappi = _raveio.open('data/cappi_DR.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

ROPO:ed reflectivity

+
+
+
cappi = _raveio.open('data/cappi_DBZH_ropo.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [12], in <module>
+----> 1 cappi = _raveio.open('data/cappi_DBZH_ropo.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+

DRQC:ed reflectivity

+
+
+
cappi = _raveio.open('data/cappi_DBZH_drqc.h5').object
+display(cappi)
+
+
+
+
+
---------------------------------------------------------------------------
+OSError                                   Traceback (most recent call last)
+Input In [13], in <module>
+----> 1 cappi = _raveio.open('data/cappi_DBZH_drqc.h5').object
+      2 display(cappi)
+
+OSError: Failed to open file
+
+
+
+
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

Compositing with BALTRAD

+
+ + +
+

next

+

BALTRAD I/O model - making sense out of data and metadata

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/BALTRAD IO.html b/_preview/104/notebooks/baltrad_short_course/BALTRAD IO.html new file mode 100644 index 00000000..344845fb --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/BALTRAD IO.html @@ -0,0 +1,1015 @@ + + + + + + + + BALTRAD I/O model - making sense out of data and metadata — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

BALTRAD I/O model - making sense out of data and metadata

+
+

Import the file I/O module along with the main RAVE module containing useful constants

+
+
+
%matplotlib inline
+import _raveio, _rave
+
+
+
+
+
+
+

Read an input ODIM_H5 file

+
+
+
rio = _raveio.open("data/201405190715_SUR.h5")
+
+
+
+
+
+
+

What is the payload in the I/O container?

+
+
+
rio.objectType is _rave.Rave_ObjectType_PVOL
+
+
+
+
+
True
+
+
+
+
+
+
+

How many scans does this volume contain?

+
+
+
pvol = rio.object
+print("%i scans in polar volume" % pvol.getNumberOfScans())
+
+
+
+
+
8 scans in polar volume
+
+
+
+
+
+
+

Ascending or descending scan strategy?

+
+
+
pvol.isAscendingScans()
+
+
+
+
+
True
+
+
+
+
+
+
+

Where is this site?

+
+

Note that all angles are represented internally in radians

+
+
+
from Proj import rd
+print("Site is located at %2.3f° lon, %2.3f° lat and %3.1f masl" % (pvol.longitude*rd, pvol.latitude*rd, pvol.height))
+print("Site's ODIM source identifiers are: %s" % pvol.source)
+
+
+
+
+
Site is located at 25.519° lon, 58.482° lat and 157.0 masl
+Site's ODIM source identifiers are: WMO:26232,RAD:EE41,PLC:Sürgavere,NOD:eesur
+
+
+
+
+
+
+
+

Access lowest scan and query some characteristics

+
+
+
scan = pvol.getScan(0)
+nrays, nbins = scan.nrays, scan.nbins
+print("Elevation angle %2.1f°" % (scan.elangle*rd))
+print("%i rays per sweep" % nrays)
+print("%i bins per ray" % nbins)
+print("%3.1f meter range bins" % scan.rscale)
+print("First ray scanned is ray %i (indexing starts at 0)" % scan.a1gate)
+print("Data acquisition started on %s:%sZ" % (scan.startdate, scan.starttime))
+print("Data acquisition ended on %s:%sZ" % (scan.enddate, scan.endtime))
+print("Scan contains %i quantities: %s" % (len(scan.getParameterNames()), scan.getParameterNames()))
+
+
+
+
+
Elevation angle 0.5°
+360 rays per sweep
+831 bins per ray
+300.0 meter range bins
+First ray scanned is ray 189 (indexing starts at 0)
+Data acquisition started on 20140519:071509Z
+Data acquisition ended on 20140519:071537Z
+Scan contains 10 quantities: ['DBZH', 'RHOHV', 'HCLASS', 'WRADH', 'PHIDP', 'ZDR', 'SQIH', 'KDP', 'VRADH', 'TH']
+
+
+
+
+
+
+

Access horizontal reflectivity and query some characteristics

+
+
+
dbzh = scan.getParameter("DBZH")
+print("Quantity is %s" % dbzh.quantity)
+print("8-bit unsigned byte data? %s" % str(dbzh.datatype is _rave.RaveDataType_UCHAR))
+print("Linear scaling coefficients from 0-255 to dBZ: gain=%2.1f, offset=%2.1f" % (dbzh.gain, dbzh.offset))
+print("Unradiated areas = %2.1f, radiated areas with no echo = %2.1f" % (dbzh.nodata, dbzh.undetect))
+
+dbzh_data = dbzh.getData()  # Accesses the NumPy array containing the reflectivities
+print("NumPy array's dimensions = %s and type = %s" % (str(dbzh_data.shape), dbzh_data.dtype))
+
+
+
+
+
Quantity is DBZH
+8-bit unsigned byte data? True
+Linear scaling coefficients from 0-255 to dBZ: gain=0.5, offset=-32.0
+Unradiated areas = 255.0, radiated areas with no echo = 0.0
+NumPy array's dimensions = (360, 831) and type = uint8
+
+
+
+
+
+
+

A primitive visualizer for plotting B-scans

+
+
+
# Convenience functionality. First convert a palette from GoogleMapsPlugin for use with matplotlib
+import matplotlib
+from GmapColorMap import dbzh as pal
+colorlist = []
+for i in range(0, len(pal), 3):
+    colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])
+
+# Then create a simple plotter
+import matplotlib.pyplot as plt
+def plot(data):
+    fig = plt.figure(figsize=(16,12))
+    plt.title("B-scan")
+    plt.imshow(data, cmap=matplotlib.colors.ListedColormap(colorlist), clim=(0,255))
+    plt.colorbar(shrink=float(nrays)/nbins)
+
+
+
+
+
+
+
plot(dbzh_data)
+
+
+
+
+../../_images/BALTRAD IO_20_0.png +
+
+
+
+

Management of optional metadata

+
+

While manadatory metadata are represented as object attributes in Python, optional metadata are not!

+
+
+
print("Polar volume has %i optional attributes" % len(pvol.getAttributeNames()))
+print("Polar scan has %i optional attributes" % len(scan.getAttributeNames()))
+print("Quantity %s has %i optional attributes" % (dbzh.quantity, len(dbzh.getAttributeNames())))
+
+print("Mandatory attribute: beamwidth is %2.1f°" % (pvol.beamwidth*rd))
+print("Optional attributes: Radar is a %s running %s" % (pvol.getAttribute("how/system"), pvol.getAttribute("how/software")))
+
+
+
+
+
Polar volume has 14 optional attributes
+Polar scan has 36 optional attributes
+Quantity DBZH has 3 optional attributes
+Mandatory attribute: beamwidth is 1.0°
+Optional attributes: Radar is a VAISWRM200 running IRIS
+
+
+
+
+
+
+

Add a bogus attribute

+
+
+
dbzh.foo = "bar"
+
+
+
+
+
---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Input In [12], in <module>
+----> 1 dbzh.foo = "bar"
+
+AttributeError: foo
+
+
+
+
+
+
+
dbzh.addAttribute("how/foo", "bar")
+print("Quantity %s now has %i optional attributes" % (dbzh.quantity, len(dbzh.getAttributeNames())))
+
+
+
+
+
Quantity DBZH now has 4 optional attributes
+
+
+
+
+
+
+
+

Create an empty parameter and populate it

+
+
+
import _polarscanparam
+param = _polarscanparam.new()
+param.quantity = "DBZH"
+param.nodata, param.undetect = 255.0, 0.0
+param.gain, param.offset = 0.4, -30.0
+
+import numpy
+data = numpy.zeros((420,500), numpy.uint8)
+param.setData(data)
+
+
+
+
+
+
+

Create an empty scan and add the parameter to it

+
+
+
import _polarscan
+from Proj import dr
+newscan = _polarscan.new()
+newscan.elangle = 25.0*dr
+newscan.addAttribute("how/simulated", "True")
+
+newscan.addParameter(param)
+print("%i rays per sweep" % newscan.nrays)
+print("%i bins per ray" % newscan.nbins)
+
+
+
+
+
420 rays per sweep
+500 bins per ray
+
+
+
+
+
+

See how the parameter’s dimensions were passed along to the scan, so they don’t have to be set explicitly. Nevertheless, plenty of metadata must be handled explicitly or ODIM_H5 files risk being incomplete.

+
+
+
newscan.a1gate = 0
+newscan.beamwidth = 1.0*dr
+newscan.rscale = 500.0
+newscan.rstart = 0.0  # Distance in meters to the start of the first range bin, unknown=0.0
+newscan.startdate = "20140831"
+newscan.starttime = "145005"
+newscan.enddate = "20140831"
+newscan.endtime = "145020"
+
+# Top-level attributes
+newscan.date = "20140831"
+newscan.time = "145000"
+newscan.source = "WMO:26232,RAD:EE41,PLC:Sürgavere,NOD:eesur"
+newscan.longitude = 25.519*dr
+newscan.latitude = 58.482*dr
+newscan.height = 157.0
+
+
+
+
+
+
+
+

Now create a new I/O container and write the scan to ODIM_H5 file.

+
+
+
container = _raveio.new()
+container.object = newscan
+container.save("data/myscan.h5")
+
+import os
+print("ODIM_H5 file is %i bytes large" % os.path.getsize("data/myscan.h5"))
+
+
+
+
+
ODIM_H5 file is 4721 bytes large
+
+
+
+
+
+

Remove compression. It makes file I/O faster. You can also tune HDF5 file-creation properties through the I/O container object.

+
+
+
container.compression_level = 0  # ZLIB compression levels 0-9
+container.save("data/myscan.h5")
+print("ODIM_H5 file is now %i bytes large" % os.path.getsize("data/myscan.h5"))
+
+
+
+
+
ODIM_H5 file is now 214320 bytes large
+
+
+
+
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan

+
+ + +
+

next

+

BALTRAD parallel processing

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/BALTRAD QC.html b/_preview/104/notebooks/baltrad_short_course/BALTRAD QC.html new file mode 100644 index 00000000..94dca331 --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/BALTRAD QC.html @@ -0,0 +1,986 @@ + + + + + + + + BALTRAD Quality Control — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

BALTRAD Quality Control

+
+

Import the file I/O module along with the main RAVE module containing useful constants

+
+
+
%matplotlib inline
+import matplotlib
+import _raveio, _rave
+
+
+
+
+
+
+

Read an input ODIM_H5 file

+
+
+
rio = _raveio.open("data/201405190715_SUR.h5")
+
+
+
+
+
+
+

Create a simple plotter for B-scans, elaborating the example from the I/O exercise

+
+
+
# Two color palettes, one used in GoogleMapsPlugin, and the other from RAVE
+from GmapColorMap import dbzh as dbzp
+from rave_win_colors import continuous_MS as vradp
+
+# Convert a 768-list palette to a matplotlib colorlist
+def make_colorlist(pal):
+    colorlist = []
+    for i in range(0, len(pal), 3):
+        colorlist.append([pal[i]/255.0, pal[i+1]/255.0, pal[i+2]/255.0])
+    return colorlist
+
+# Convert lists to colormaps
+dbzcl = make_colorlist(dbzp)
+vradcl = make_colorlist(vradp)
+
+# Then create a simple plotter
+import matplotlib.pyplot as plt
+#from types import StringType
+StringType = type('')
+def plot(data, colorlist=dbzcl, title="B-scan"):
+    mini, maxi = data.shape.index(min(data.shape)), data.shape.index(max(data.shape))
+    figsize=(16,12) if mini == 0 else (12,8)
+    fig = plt.figure(figsize=figsize)
+    plt.title(title)
+    clist=colorlist if type(colorlist)==StringType else matplotlib.colors.ListedColormap(colorlist)
+    plt.imshow(data, cmap=clist, clim=(0,255))
+    plt.colorbar(shrink=float(data.shape[mini])/data.shape[maxi])
+
+
+
+
+
+
+

Access the polar volume and plot VRAD data from the lowest scan

+
+
+
pvol = rio.object
+plot(pvol.getScan(0).getParameter("VRADH").getData(), vradcl, "Original VRAD")
+
+
+
+
+../../_images/BALTRAD QC_8_0.png +
+
+
+
+

Dealias the volume

+
+
+
import _dealias
+ret = _dealias.dealias(pvol)
+
+
+
+
+
+

Check whether the first scan’s been dealiased

+
+
+
print("This first scan is dealiased: %s" % str(_dealias.dealiased(pvol.getScan(0))))
+
+
+
+
+
This first scan is dealiased: True
+
+
+
+
+
+
+

Replot for comparison

+
+
+
plot(pvol.getScan(0).getParameter("VRADH").getData(), vradcl, "Dealiased VRAD")
+
+
+
+
+../../_images/BALTRAD QC_14_0.png +
+
+
+
+
+

Shift gears - back to reflectivity

+
+
+
rio = _raveio.open("data/plrze_pvol_20120205T0430Z.h5")
+pvol = rio.object
+plot(pvol.getScan(0).getParameter("DBZH").getData(), title="Original DBZH")
+
+
+
+
+../../_images/BALTRAD QC_16_0.png +
+
+
+
+

Use the bRopo package’s quality plugin to identify and remove non-precipitation echoes

+
+
+
import odc_polarQC
+import warnings
+warnings.filterwarnings('ignore')  # Suppress SyntaxWarning from Python2 code
+
+odc_polarQC.algorithm_ids = ["ropo"]
+pvol = odc_polarQC.QC(pvol)
+
+
+
+
+
+

Plot the resulting DBZH

+
+
+
plot(pvol.getScan(0).getParameter("DBZH").getData(), title="DBZH after bRopo")
+
+
+
+
+../../_images/BALTRAD QC_20_0.png +
+
+
+
+
+

Topographical beam-blockage QC using the beamb package’s quality plugin

+
+
+
import time
+odc_polarQC.algorithm_ids = ["beamb"]
+before = time.time()
+pvol = odc_polarQC.QC(pvol)
+after = time.time()
+print("beamb runtime = %2.2f seconds" % (after-before))
+
+
+
+
+
beamb runtime = 6.71 seconds
+
+
+
+
+
+
+

Probability of overshooting

+
+
+
odc_polarQC.algorithm_ids = ["rave-overshooting"]
+pvol = odc_polarQC.QC(pvol)
+
+
+
+
+
+
+

Accessing and manging data quality fields

+
+
+
scan = pvol.getScan(0)
+print("Scan contains %i quality fields" % scan.getNumberOfQualityFields())
+
+
+
+
+
Scan contains 3 quality fields
+
+
+
+
+
+
+
for i in range(scan.getNumberOfQualityFields()):
+    qf = scan.getQualityField(i)
+    print("Quality field %i has identifier %s" % (i, qf.getAttribute("how/task")))
+
+
+
+
+
Quality field 0 has identifier fi.fmi.ropo.detector.classification
+Quality field 1 has identifier se.smhi.detector.beamblockage
+Quality field 2 has identifier se.smhi.detector.poo
+
+
+
+
+
+
+

Plot quality fields

+
+

Beam blockage

+
+
+
bb = scan.getQualityFieldByHowTask("se.smhi.detector.beamblockage")
+plot(bb.getData(), "binary", "Quality indicator for beam blockage")
+
+
+
+
+../../_images/BALTRAD QC_30_0.png +
+
+
+
+

Probability of non-precipitation

+
+
+
bb = scan.getQualityFieldByHowTask("fi.fmi.ropo.detector.classification")
+plot(bb.getData(), "binary", "Quality indicator for ropo")
+
+
+
+
+../../_images/BALTRAD QC_32_0.png +
+
+
+
+

Probability of overshooting

+
+
+
bb = scan.getQualityFieldByHowTask("se.smhi.detector.poo")
+plot(bb.getData(), "binary", "Quality indicator for PoO")
+
+
+
+
+../../_images/BALTRAD QC_34_0.png +
+
+
+
+
+

Chaining algorithms - new data

+
+
+
rio = _raveio.open("data/sekir.h5")
+pvol = rio.object
+
+odc_polarQC.algorithm_ids = ["ropo", "beamb", "radvol-att", "radvol-broad", "rave-overshooting"]
+pvol = odc_polarQC.QC(pvol)
+
+
+
+
+
+
+
scan = pvol.getScan(0)
+att = scan.getQualityField(2)
+plot(att.getData(), "binary", "Attenuation")
+
+
+
+
+../../_images/BALTRAD QC_37_0.png +
+
+
+
+

“Total Quality”

+
+
+
odc_polarQC.algorithm_ids = ["qi-total"]
+pvol = odc_polarQC.QC(pvol)
+
+
+
+
+
+
+
qitot = scan.getQualityField(5)
+plot(qitot.getData(), "binary", "Total quality index")
+
+
+
+
+../../_images/BALTRAD QC_40_0.png +
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

BALTRAD parallel processing

+
+ + +
+

next

+

LROSE Tutorial

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/BALTRAD parallel processing.html b/_preview/104/notebooks/baltrad_short_course/BALTRAD parallel processing.html new file mode 100644 index 00000000..45f85ee5 --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/BALTRAD parallel processing.html @@ -0,0 +1,754 @@ + + + + + + + + BALTRAD parallel processing — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

BALTRAD parallel processing

+

The default VM setup is to use a single CPU core. In order to demonstrate the power of parallel processing, you must first determine whether your physical hardware has more than a single core.

+

On Linux this is done in the terminal with the ‘nproc’ command.

+

On Mac this is done in the terminal with the ‘sysctl -n hw.ncpu’ command.

+

On Windows this is done graphically using the Task Manager’s Performance tab.

+

We want tune our VM to harness the power of several CPUs. Follow the following steps:

+
    +

  1. Shut down the IPython notebook Server (Ctrl-C, answer yes)

    2. +

  2. Shutdown the VM (click the X button in the VM window, choose power down the machine)

    3. +

  3. Select the VM in the VirtualBox Manager Window, from the menu choose Machine->Setting

    4. +

  4. Choose the System Tab, then Processor, use the slider to set the number of Processor to 2, 4, or 8 depending on your system resources.

    5. +

  5. Click Ok, and then start the machine

    6. +

  6. Login, use the start_notebook.sh script to start the IPython server, start the notebook and you should have multiple processors!

    7. +
+

RELOAD THIS PAGE!

+
+

Verify from Python the number of CPU cores at our disposal

+
+
+
import multiprocessing
+print("We have %i cores to play with!" % multiprocessing.cpu_count())
+
+
+
+
+
We have 4 cores to play with!
+
+
+
+
+

Yay! Now we’re going to set up some rudimentary functionality that will allow us to distribute a processing load among our cores.

+
+
+

Define a generator

+
+
+
import os
+import _raveio, odc_polarQC
+
+# Specify the processing chain
+odc_polarQC.algorithm_ids = ["ropo", "beamb", "radvol-att", "radvol-broad", "rave-overshooting", "qi-total"]
+
+# Run processing chain on a single file. Return an output file string.
+def generate(file_string):
+    rio = _raveio.open(file_string)
+
+    pvol = rio.object
+    pvol = odc_polarQC.QC(pvol)
+    rio.object = pvol
+    
+    # Derive an output file name
+    path, fstr = os.path.split(file_string)
+    ofstr = os.path.join(path, 'qc_'+fstr)
+    
+    rio.save(ofstr)
+    return ofstr
+
+
+
+
+
+
+

Feed the generator, sequentially

+
+
+
import glob, time
+
+ifstrs = glob.glob("data/se*.h5")
+before = time.time()
+for fstr in ifstrs:
+    print(fstr, generate(fstr))
+after = time.time()
+
+print("Processing time: %3.2f seconds" % (after-before))
+
+
+
+
+
data/sekkr.h5 data/qc_sekkr.h5
+
+
+
data/sevar.h5 data/qc_sevar.h5
+
+
+
data/sehud.h5 data/qc_sehud.h5
+
+
+
data/sease.h5 data/qc_sease.h5
+
+
+
data/selek.h5 data/qc_selek.h5
+
+
+
data/seang.h5 data/qc_seang.h5
+
+
+
data/seosu.h5 data/qc_seosu.h5
+
+
+
data/sevil.h5 data/qc_sevil.h5
+
+
+
data/selul.h5 data/qc_selul.h5
+
+
+
data/sekir.h5 data/qc_sekir.h5
+
+
+
data/searl.h5 data/qc_searl.h5
+Processing time: 5.89 seconds
+
+
+
+
+

Mental note: repeat once!

+
+
+

Multiprocess the generator

+
+
+
# Both input and output are a list of file strings
+def multi_generate(fstrs, procs=None):
+    pool = multiprocessing.Pool(procs)  # Pool of processors. Defaults to all available logical cores
+
+    results = []
+    # chunksize=1 means feed a process a new job as soon as the process is idle.
+    # In our case, this restricts the queue to one "dispatcher" which is faster.
+    r = pool.map_async(generate, fstrs, chunksize=1, callback=results.append)
+    r.wait()
+
+    return results[0]
+
+
+
+
+
+
+

Feed the monster, asynchronously!

+
+
+
before = time.time()
+ofstrs = multi_generate(ifstrs)
+after = time.time()
+
+print("Processing time: %3.2f seconds" % (after-before))
+
+
+
+
+
Processing time: 2.68 seconds
+
+
+
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

BALTRAD I/O model - making sense out of data and metadata

+
+ + +
+

next

+

BALTRAD Quality Control

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/baltrad_short_course/README.html b/_preview/104/notebooks/baltrad_short_course/README.html new file mode 100644 index 00000000..bffc50d1 --- /dev/null +++ b/_preview/104/notebooks/baltrad_short_course/README.html @@ -0,0 +1,552 @@ + + + + + + + + baltrad_short_course — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

baltrad_short_course

+

IPy Notebook exercises and data for the BALTRAD Toolbox

+
+ + + + +
+ +
+
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/environment.html b/_preview/104/notebooks/environment.html new file mode 100644 index 00000000..add13aca --- /dev/null +++ b/_preview/104/notebooks/environment.html @@ -0,0 +1,1242 @@ + + + + + + + + Environment overview — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

Environment overview

+
+
+
!env
+
+
+
+
+
CONDA_SHLVL=1
+PYTHONUNBUFFERED=1
+PYDEVD_USE_FRAME_EVAL=NO
+LC_ALL=en_US.UTF-8
+LD_LIBRARY_PATH=:/srv/conda/envs/notebook/lib:/srv/conda/envs/notebook/hlhdf/lib:/srv/conda/envs/notebook/rave/lib:/srv/conda/envs/notebook/beamb/lib:/srv/conda/envs/notebook/bropo/lib:/srv/conda/envs/notebook/baltrad-wrwp/lib
+APP_BASE=/srv
+KERNEL_PYTHON_PREFIX=/srv/conda/envs/notebook
+CONDA_EXE=/srv/conda/bin/conda
+GDAL_DATA=/srv/conda/envs/notebook/share/gdal
+host_alias=x86_64-conda-linux-gnu
+GPROF=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gprof
+_=/srv/conda/envs/notebook/bin/env
+LANG=en_US.UTF-8
+GXX=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-g++
+GDAL_DRIVER_PATH=/srv/conda/envs/notebook/lib/gdalplugins
+NPM_DIR=/srv/npm
+HOSTNAME=05c47e7c7add
+LD_GOLD=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ld.gold
+CMAKE_ARGS=-DCMAKE_AR=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ar -DCMAKE_CXX_COMPILER_AR=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ar -DCMAKE_C_COMPILER_AR=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ar -DCMAKE_RANLIB=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ranlib -DCMAKE_CXX_COMPILER_RANLIB=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ranlib -DCMAKE_C_COMPILER_RANLIB=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ranlib -DCMAKE_LINKER=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ld -DCMAKE_STRIP=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-strip
+DEBUG_CPPFLAGS=-D_DEBUG -D_FORTIFY_SOURCE=2 -Og -isystem /srv/conda/envs/notebook/include
+DEBUG_CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+GCC_NM=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-nm
+GSETTINGS_SCHEMA_DIR_CONDA_BACKUP=
+CPL_ZIP_ENCODING=UTF-8
+CONDA_PREFIX=/srv/conda/envs/notebook
+GCC_RANLIB=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ranlib
+CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+CONDA_DIR=/srv/conda
+GCC_AR=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc-ar
+RAVEROOT=/srv/conda/envs/notebook
+FC_FOR_BUILD=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gfortran
+MAMBA_EXE=/srv/conda/bin/mamba
+CONDA_TOOLCHAIN_HOST=x86_64-conda-linux-gnu
+_CE_M=
+CC=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-cc
+READELF=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-readelf
+PROJ_LIB=/srv/conda/envs/notebook/share/proj
+USER=jovyan
+CONDA_TOOLCHAIN_BUILD=x86_64-conda-linux-gnu
+CXXFLAGS=-fvisibility-inlines-hidden -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+PAGER=cat
+STRIP=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-strip
+DEBUG_FFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments -ffunction-sections -pipe
+OBJCOPY=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-objcopy
+_CONDA_PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_x86_64_conda_cos6_linux_gnu
+JPY_PARENT_PID=1
+PWD=/work/notebooks
+HOME=/home/jovyan
+CONDA_PYTHON_EXE=/srv/conda/bin/python
+ADDR2LINE=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-addr2line
+CMAKE_PREFIX_PATH=/srv/conda/envs/notebook:/srv/conda/envs/notebook/x86_64-conda-linux-gnu/sysroot/usr
+HOST=x86_64-conda-linux-gnu
+CLICOLOR=1
+F77=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gfortran
+NB_ENVIRONMENT_FILE=/tmp/env/environment.lock
+FORTRANFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+DEBIAN_FRONTEND=noninteractive
+RANLIB=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ranlib
+OBJDUMP=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-objdump
+AS=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-as
+AR=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ar
+GFORTRAN=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gfortran
+_CE_CONDA=
+GSETTINGS_SCHEMA_DIR=/srv/conda/envs/notebook/share/glib-2.0/schemas
+CC_FOR_BUILD=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-cc
+build_alias=x86_64-conda-linux-gnu
+DEBUG_FORTRANFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments -ffunction-sections -pipe
+PROJ_NETWORK=ON
+BUILD=x86_64-conda-linux-gnu
+FC=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gfortran
+NM=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-nm
+CONDA_PROMPT_MODIFIER=(notebook) 
+CONDA_BUILD_SYSROOT=/srv/conda/envs/notebook/x86_64-conda-linux-gnu/sysroot
+DEBUG_CXXFLAGS=-fvisibility-inlines-hidden -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+CXX=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-c++
+TERM=xterm-color
+SHELL=/bin/bash
+CXXFILT=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-c++filt
+MAMBA_ROOT_PREFIX=/srv/conda
+MPLBACKEND=module://matplotlib_inline.backend_inline
+NB_PYTHON_PREFIX=/srv/conda/envs/notebook
+ELFEDIT=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-elfedit
+SHLVL=1
+LANGUAGE=en_US.UTF-8
+FFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /srv/conda/envs/notebook/include
+CPPFLAGS=-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /srv/conda/envs/notebook/include
+REPO_DIR=/home/jovyan
+STRINGS=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-strings
+F90=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gfortran
+F95=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-f95
+LDFLAGS=-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,--allow-shlib-undefined -Wl,-rpath,/srv/conda/envs/notebook/lib -Wl,-rpath-link,/srv/conda/envs/notebook/lib -L/srv/conda/envs/notebook/lib
+CXX_FOR_BUILD=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-c++
+PATH=:/srv/conda/envs/notebook/bin:/srv/conda/condabin:/home/jovyan/.local/bin:/home/jovyan/.local/bin:/srv/conda/envs/notebook/bin:/srv/conda/bin:/srv/npm/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/srv/conda/envs/notebook/hlhdf/bin:/srv/conda/envs/notebook/rave/bin:/srv/conda/envs/notebook/beamb/bin:/srv/conda/envs/notebook/bropo/bin:/srv/conda/envs/notebook/baltrad-wrwp/bin
+GCC=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-gcc
+NPM_CONFIG_GLOBALCONFIG=/srv/npm/npmrc
+CONDA_DEFAULT_ENV=notebook
+SIZE=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-size
+CPP=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-cpp
+XML_CATALOG_FILES=file:///srv/conda/envs/notebook/etc/xml/catalog file:///etc/xml/catalog
+LD=/srv/conda/envs/notebook/bin/x86_64-conda-linux-gnu-ld
+GIT_PAGER=cat
+
+
+
+
+
+
+
!conda list
+
+
+
+
+
# packages in environment at /srv/conda/envs/notebook:
+#
+# Name                    Version                   Build  Channel
+_libgcc_mutex             0.1                 conda_forge    conda-forge
+_openmp_mutex             4.5                       1_gnu    conda-forge
+affine                    2.4.0              pyhd8ed1ab_0    conda-forge
+aiobotocore               2.11.2             pyhd8ed1ab_0    conda-forge
+aiohttp                   3.9.3            py39hd1e30aa_0    conda-forge
+aioitertools              0.11.0             pyhd8ed1ab_0    conda-forge
+aiosignal                 1.3.1              pyhd8ed1ab_0    conda-forge
+alabaster                 0.7.16             pyhd8ed1ab_0    conda-forge
+alembic                   1.7.5              pyhd8ed1ab_0    conda-forge
+alsa-lib                  1.2.8                h166bdaf_0    conda-forge
+anyio                     3.5.0            py39hf3d152e_0    conda-forge
+appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge
+argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
+argon2-cffi-bindings      21.2.0           py39h3811e60_1    conda-forge
+arm_pyart                 1.12.7           py39hb9d737c_0    conda-forge
+arrow                     1.3.0              pyhd8ed1ab_0    conda-forge
+asttokens                 2.0.5              pyhd8ed1ab_0    conda-forge
+async-timeout             4.0.3              pyhd8ed1ab_0    conda-forge
+async_generator           1.10                       py_0    conda-forge
+attr                      2.5.1                h166bdaf_1    conda-forge
+attrs                     21.4.0             pyhd8ed1ab_0    conda-forge
+autoconf                  2.71            pl5321h2b4cb7a_1    conda-forge
+automake                  1.16.5          pl5321ha770c72_0    conda-forge
+babel                     2.9.1              pyh44b312d_0    conda-forge
+backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
+backports                 1.0                        py_2    conda-forge
+backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
+bash                      5.2.21               h7f99829_0    conda-forge
+beautifulsoup4            4.12.3             pyha770c72_0    conda-forge
+binaryornot               0.4.4                      py_1    conda-forge
+binutils                  2.36.1               hdd6e379_2    conda-forge
+binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
+binutils_linux-64         2.36                hf3e587d_10    conda-forge
+black                     21.12b0            pyhd8ed1ab_0    conda-forge
+bleach                    4.1.0              pyhd8ed1ab_0    conda-forge
+blinker                   1.4                        py_1    conda-forge
+blosc                     1.21.5               h0f2a231_0    conda-forge
+bokeh                     2.4.3              pyhd8ed1ab_3    conda-forge
+boost-cpp                 1.78.0               h5adbc97_2    conda-forge
+botocore                  1.34.34            pyhd8ed1ab_0    conda-forge
+branca                    0.7.1              pyhd8ed1ab_0    conda-forge
+brotli                    1.1.0                hd590300_1    conda-forge
+brotli-bin                1.1.0                hd590300_1    conda-forge
+brotlipy                  0.7.0           py39h3811e60_1003    conda-forge
+bzip2                     1.0.8                h7f98852_4    conda-forge
+c-ares                    1.18.1               h7f98852_0    conda-forge
+c-compiler                1.7.0                hd590300_0    conda-forge
+ca-certificates           2021.10.8            ha878542_0    conda-forge
+cached-property           1.5.2                hd8ed1ab_1    conda-forge
+cached_property           1.5.2              pyha770c72_1    conda-forge
+cairo                     1.16.0            ha61ee94_1014    conda-forge
+cartopy                   0.20.3           py39hed214b2_2    conda-forge
+certifi                   2021.10.8        py39hf3d152e_1    conda-forge
+certipy                   0.1.3                      py_0    conda-forge
+cffi                      1.15.0           py39h4bc2ebd_0    conda-forge
+cfitsio                   4.1.0                hd9d235c_0    conda-forge
+cftime                    1.6.3            py39h44dd56e_0    conda-forge
+chardet                   5.2.0            py39hf3d152e_1    conda-forge
+charset-normalizer        2.0.10             pyhd8ed1ab_0    conda-forge
+click                     8.0.3            py39hf3d152e_1    conda-forge
+click-completion          0.5.2            py39hf3d152e_6    conda-forge
+click-log                 0.4.0              pyhd8ed1ab_0    conda-forge
+click-plugins             1.1.1                      py_0    conda-forge
+cligj                     0.7.2              pyhd8ed1ab_1    conda-forge
+cloudpickle               3.0.0              pyhd8ed1ab_0    conda-forge
+colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
+colorcet                  3.0.1              pyhd8ed1ab_0    conda-forge
+compilers                 1.7.0                ha770c72_0    conda-forge
+cookiecutter              2.1.1              pyh6c4a22f_0    conda-forge
+coreutils                 9.4                  hd590300_0    conda-forge
+cryptography              36.0.1           py39h95dcef6_0    conda-forge
+curl                      7.86.0               h7bff187_1    conda-forge
+cxx-compiler              1.7.0                h00ab1b0_0    conda-forge
+cycler                    0.12.1             pyhd8ed1ab_0    conda-forge
+cython                    3.0.8            py39h3d6467e_0    conda-forge
+cytoolz                   0.12.3           py39hd1e30aa_0    conda-forge
+dask                      2022.8.1           pyhd8ed1ab_2    conda-forge
+dask-core                 2022.8.1           pyhd8ed1ab_0    conda-forge
+dataclasses               0.8                pyhc8e2a94_3    conda-forge
+datashader                0.14.2             pyh6c4a22f_0    conda-forge
+datashape                 0.5.4                      py_1    conda-forge
+dbus                      1.13.6               h5008d03_3    conda-forge
+debugpy                   1.5.1            py39he80948d_0    conda-forge
+decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
+defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
+deprecation               2.1.0              pyh9f0ad1d_0    conda-forge
+distributed               2022.8.1           pyhd8ed1ab_2    conda-forge
+docutils                  0.16             py39hf3d152e_4    conda-forge
+entrypoints               0.3             pyhd8ed1ab_1003    conda-forge
+executing                 0.8.2              pyhd8ed1ab_0    conda-forge
+expat                     2.5.0                hcb278e6_1    conda-forge
+fftw                      3.3.10          nompi_hc118613_108    conda-forge
+fiona                     1.8.22           py39h80939cc_0    conda-forge
+flit-core                 3.6.0              pyhd8ed1ab_0    conda-forge
+folium                    0.15.1             pyhd8ed1ab_0    conda-forge
+font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    conda-forge
+font-ttf-inconsolata      3.000                h77eed37_0    conda-forge
+font-ttf-source-code-pro  2.038                h77eed37_0    conda-forge
+font-ttf-ubuntu           0.83                 h77eed37_1    conda-forge
+fontconfig                2.14.2               h14ed4e7_0    conda-forge
+fonts-conda-ecosystem     1                             0    conda-forge
+fonts-conda-forge         1                             0    conda-forge
+fonttools                 4.49.0           py39hd1e30aa_0    conda-forge
+fortran-compiler          1.7.0                heb67821_0    conda-forge
+freetype                  2.12.1               h267a509_2    conda-forge
+freexl                    1.0.6                h166bdaf_1    conda-forge
+frozenlist                1.4.1            py39hd1e30aa_0    conda-forge
+fsspec                    2024.2.0           pyhca7485f_0    conda-forge
+gcc                       12.1.0              h9ea6d83_10    conda-forge
+gcc_impl_linux-64         12.1.0              hea43390_17    conda-forge
+gcc_linux-64              12.1.0              h3bb4806_10    conda-forge
+gdal                      3.5.2            py39h92c1d47_4    conda-forge
+geopandas                 0.11.1             pyhd8ed1ab_0    conda-forge
+geopandas-base            0.11.1             pyha770c72_0    conda-forge
+geos                      3.11.0               h27087fc_0    conda-forge
+geotiff                   1.7.1                h4fc65e6_3    conda-forge
+gettext                   0.21.1               h27087fc_0    conda-forge
+gfortran                  12.1.0              h6fcb4f1_10    conda-forge
+gfortran_impl_linux-64    12.1.0              h1db8e46_17    conda-forge
+gfortran_linux-64         12.1.0              h7ec3724_10    conda-forge
+giflib                    5.2.1                h0b41bf4_3    conda-forge
+gitdb                     4.0.11             pyhd8ed1ab_0    conda-forge
+gitpython                 3.1.42             pyhd8ed1ab_0    conda-forge
+glib                      2.78.4               hfc55251_0    conda-forge
+glib-tools                2.78.4               hfc55251_0    conda-forge
+gnuconfig                 2020.11.07           hd8ed1ab_0    conda-forge
+greenlet                  1.1.2            py39he80948d_1    conda-forge
+gst-plugins-base          1.21.3               h4243ec0_1    conda-forge
+gstreamer                 1.21.3               h25f0c4b_1    conda-forge
+gstreamer-orc             0.4.37               hd590300_0    conda-forge
+gxx                       12.1.0              h9ea6d83_10    conda-forge
+gxx_impl_linux-64         12.1.0              hea43390_17    conda-forge
+gxx_linux-64              12.1.0              h1f501c1_10    conda-forge
+h5netcdf                  1.3.0              pyhd8ed1ab_0    conda-forge
+h5py                      3.7.0           nompi_py39h817c9c5_102    conda-forge
+hdf4                      4.2.15               h9772cbc_5    conda-forge
+hdf5                      1.12.2          nompi_h2386368_100    conda-forge
+holoviews                 1.17.1             pyhd8ed1ab_0    conda-forge
+hvplot                    0.8.0              pyh6c4a22f_0    conda-forge
+icu                       70.1                 h27087fc_0    conda-forge
+idna                      3.3                pyhd8ed1ab_0    conda-forge
+imagesize                 1.4.1              pyhd8ed1ab_0    conda-forge
+importlib-metadata        4.10.1           py39hf3d152e_0    conda-forge
+importlib_metadata        4.10.1               hd8ed1ab_0    conda-forge
+importlib_resources       5.4.0              pyhd8ed1ab_0    conda-forge
+ipykernel                 6.7.0            py39hef51801_0    conda-forge
+ipython                   8.0.1            py39hf3d152e_0    conda-forge
+ipython_genutils          0.2.0                      py_1    conda-forge
+ipywidgets                7.6.5              pyhd8ed1ab_0    conda-forge
+jack                      1.9.22               h11f4161_0    conda-forge
+jedi                      0.18.1           py39hf3d152e_0    conda-forge
+jinja2                    3.0.3              pyhd8ed1ab_0    conda-forge
+jinja2-time               0.2.0              pyhd8ed1ab_3    conda-forge
+jmespath                  1.0.1              pyhd8ed1ab_0    conda-forge
+joblib                    1.3.2              pyhd8ed1ab_0    conda-forge
+jpeg                      9e                   h0b41bf4_3    conda-forge
+json-c                    0.16                 hc379101_0    conda-forge
+json5                     0.9.5              pyh9f0ad1d_0    conda-forge
+jsonschema                3.2.0              pyhd8ed1ab_3    conda-forge
+jupyter-book              0.12.3             pyhd8ed1ab_0    conda-forge
+jupyter-cache             0.4.3              pyhd8ed1ab_0    conda-forge
+jupyter-offlinenotebook   0.2.2              pyh1d7be83_0    conda-forge
+jupyter-resource-usage    0.6.1              pyhd8ed1ab_0    conda-forge
+jupyter-server-mathjax    0.2.6              pyh5bfe37b_1    conda-forge
+jupyter-sphinx            0.3.2              pyhd8ed1ab_1    conda-forge
+jupyter_client            7.1.2              pyhd8ed1ab_0    conda-forge
+jupyter_core              4.9.1            py39hf3d152e_1    conda-forge
+jupyter_server            1.13.4             pyhd8ed1ab_0    conda-forge
+jupyter_telemetry         0.1.0              pyhd8ed1ab_1    conda-forge
+jupyterhub-base           1.4.2            py39hf3d152e_0    conda-forge
+jupyterhub-singleuser     1.4.2            py39hf3d152e_0    conda-forge
+jupyterlab                3.2.8              pyhd8ed1ab_0    conda-forge
+jupyterlab_pygments       0.1.2              pyh9f0ad1d_0    conda-forge
+jupyterlab_server         2.10.3             pyhd8ed1ab_0    conda-forge
+jupyterlab_widgets        1.0.2              pyhd8ed1ab_0    conda-forge
+kealib                    1.4.15               ha7026e8_1    conda-forge
+kernel-headers_linux-64   2.6.32              he073ed8_17    conda-forge
+keyutils                  1.6.1                h166bdaf_0    conda-forge
+kiwisolver                1.4.5            py39h7633fee_1    conda-forge
+krb5                      1.19.3               h3790be6_0    conda-forge
+lame                      3.100             h166bdaf_1003    conda-forge
+latexcodec                2.0.1              pyh9f0ad1d_0    conda-forge
+lcms2                     2.14                 h6ed2654_0    conda-forge
+ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
+lerc                      4.0.0                h27087fc_0    conda-forge
+libaec                    1.1.2                h59595ed_1    conda-forge
+libblas                   3.9.0           5_h92ddd45_netlib    conda-forge
+libbrotlicommon           1.1.0                hd590300_1    conda-forge
+libbrotlidec              1.1.0                hd590300_1    conda-forge
+libbrotlienc              1.1.0                hd590300_1    conda-forge
+libcap                    2.66                 ha37c62d_0    conda-forge
+libcblas                  3.9.0           5_h92ddd45_netlib    conda-forge
+libclang                  15.0.7          default_hb11cfb5_4    conda-forge
+libclang13                15.0.7          default_ha2b6cf4_4    conda-forge
+libcups                   2.3.3                h3e49a29_2    conda-forge
+libcurl                   7.86.0               h7bff187_1    conda-forge
+libdap4                   3.20.6               hd7c4107_2    conda-forge
+libdb                     6.2.32               h9c3ff4c_0    conda-forge
+libdeflate                1.14                 h166bdaf_0    conda-forge
+libedit                   3.1.20191231         he28a2e2_2    conda-forge
+libev                     4.33                 h516909a_1    conda-forge
+libevent                  2.1.10               h9b69904_4    conda-forge
+libexpat                  2.5.0                hcb278e6_1    conda-forge
+libffi                    3.4.2                h7f98852_5    conda-forge
+libflac                   1.4.3                h59595ed_0    conda-forge
+libgcc-devel_linux-64     12.1.0              h1ec3361_17    conda-forge
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+panel                     0.14.4             pyhd8ed1ab_0    conda-forge
+param                     1.13.0             pyh1a96a4e_0    conda-forge
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+platformdirs              2.3.0              pyhd8ed1ab_0    conda-forge
+ply                       3.11                       py_1    conda-forge
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+poppler-data              0.4.12               hd8ed1ab_0    conda-forge
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+pulseaudio                16.1                 h4ab2085_1    conda-forge
+pure_eval                 0.2.2              pyhd8ed1ab_0    conda-forge
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+pybtex-docutils           1.0.3            py39hf3d152e_1    conda-forge
+pycparser                 2.21               pyhd8ed1ab_0    conda-forge
+pyct                      0.4.6                      py_0    conda-forge
+pyct-core                 0.4.6                      py_0    conda-forge
+pycurl                    7.44.1           py39h72e3413_1    conda-forge
+pydata-sphinx-theme       0.7.2              pyhd8ed1ab_0    conda-forge
+pygments                  2.11.2             pyhd8ed1ab_0    conda-forge
+pyjwt                     2.3.0              pyhd8ed1ab_1    conda-forge
+pyopenssl                 21.0.0             pyhd8ed1ab_0    conda-forge
+pyparsing                 3.0.7              pyhd8ed1ab_0    conda-forge
+pyproj                    3.4.0            py39hdcf6798_0    conda-forge
+pyqt                      5.15.7           py39h5c7b992_3    conda-forge
+pyqt5-sip                 12.11.0          py39h227be39_3    conda-forge
+pyrad_arm                 0.6.0            py39hce5d2b2_1    conda-forge
+pyrsistent                0.18.1           py39h3811e60_0    conda-forge
+pyshp                     2.3.1              pyhd8ed1ab_0    conda-forge
+pysocks                   1.7.1            py39hf3d152e_4    conda-forge
+python                    3.9.15          h47a2c10_0_cpython    conda-forge
+python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
+python-json-logger        2.0.1              pyh9f0ad1d_0    conda-forge
+python-slugify            8.0.4              pyhd8ed1ab_0    conda-forge
+python_abi                3.9                      2_cp39    conda-forge
+pytz                      2021.3             pyhd8ed1ab_0    conda-forge
+pyviz_comms               2.3.2              pyhd8ed1ab_0    conda-forge
+pyyaml                    6.0.1            py39hd1e30aa_1    conda-forge
+pyzmq                     22.3.0           py39h37b5a0c_1    conda-forge
+qt-main                   5.15.6               h7acdfc8_2    conda-forge
+rasterio                  1.3.3            py39h498ee8f_0    conda-forge
+readline                  8.2                  h8228510_1    conda-forge
+requests                  2.27.1             pyhd8ed1ab_0    conda-forge
+rioxarray                 0.12.0             pyhd8ed1ab_0    conda-forge
+rtree                     1.2.0            py39hb102c33_0    conda-forge
+ruamel.yaml               0.17.19          py39h3811e60_0    conda-forge
+ruamel.yaml.clib          0.2.6            py39h3811e60_0    conda-forge
+s3fs                      2024.2.0           pyhd8ed1ab_0    conda-forge
+scikit-learn              1.4.1.post1      py39ha22ef79_0    conda-forge
+scipy                     1.9.1            py39h8ba3f38_0    conda-forge
+send2trash                1.8.0              pyhd8ed1ab_0    conda-forge
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+sip                       6.7.12           py39h3d6467e_0    conda-forge
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+sphinx-jupyterbook-latex  0.4.7              pyhd8ed1ab_0    conda-forge
+sphinx-multitoc-numbering 0.1.3              pyhd8ed1ab_0    conda-forge
+sphinx-panels             0.6.0              pyhd8ed1ab_0    conda-forge
+sphinx-pythia-theme       2022.3.29                pypi_0    pypi
+sphinx-thebe              0.1.2              pyhd8ed1ab_0    conda-forge
+sphinx-togglebutton       0.3.2              pyhd8ed1ab_0    conda-forge
+sphinxcontrib-applehelp   1.0.4              pyhd8ed1ab_0    conda-forge
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+sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
+sphinxcontrib-htmlhelp    2.0.1              pyhd8ed1ab_0    conda-forge
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+sphinxcontrib-serializinghtml 1.1.5              pyhd8ed1ab_2    conda-forge
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+traitlets                 5.1.1              pyhd8ed1ab_0    conda-forge
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+types-python-dateutil     2.8.19.20240106    pyhd8ed1ab_0    conda-forge
+typing-extensions         4.0.1                hd8ed1ab_0    conda-forge
+typing_extensions         4.0.1              pyha770c72_0    conda-forge
+tzcode                    2024a                h3f72095_0    conda-forge
+tzdata                    2021e                he74cb21_0    conda-forge
+uc-micro-py               1.0.3              pyhd8ed1ab_0    conda-forge
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+urllib3                   1.26.8             pyhd8ed1ab_1    conda-forge
+versioneer                0.29               pyhd8ed1ab_0    conda-forge
+wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
+webencodings              0.5.1                      py_1    conda-forge
+websocket-client          1.2.3              pyhd8ed1ab_0    conda-forge
+wheel                     0.37.1             pyhd8ed1ab_0    conda-forge
+widgetsnbextension        3.5.2            py39hf3d152e_1    conda-forge
+wradlib                   1.16.2             pyhd8ed1ab_0    conda-forge
+wrapt                     1.16.0           py39hd1e30aa_0    conda-forge
+xarray                    2022.6.0           pyhd8ed1ab_1    conda-forge
+xcb-util                  0.4.0                h516909a_0    conda-forge
+xcb-util-image            0.4.0                h166bdaf_0    conda-forge
+xcb-util-keysyms          0.4.0                h516909a_0    conda-forge
+xcb-util-renderutil       0.3.9                h166bdaf_0    conda-forge
+xcb-util-wm               0.4.1                h516909a_0    conda-forge
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+xkeyboard-config          2.38                 h0b41bf4_0    conda-forge
+xmltodict                 0.13.0             pyhd8ed1ab_0    conda-forge
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+xorg-libice               1.0.10               h7f98852_0    conda-forge
+xorg-libsm                1.2.3             hd9c2040_1000    conda-forge
+xorg-libx11               1.8.4                h0b41bf4_0    conda-forge
+xorg-libxau               1.0.11               hd590300_0    conda-forge
+xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
+xorg-libxext              1.3.4                h0b41bf4_2    conda-forge
+xorg-libxrender           0.9.10            h7f98852_1003    conda-forge
+xorg-renderproto          0.11.1            h7f98852_1002    conda-forge
+xorg-xextproto            7.3.0             h0b41bf4_1003    conda-forge
+xorg-xproto               7.0.31            h7f98852_1007    conda-forge
+xyzservices               2023.10.1          pyhd8ed1ab_0    conda-forge
+xz                        5.2.6                h166bdaf_0    conda-forge
+yaml                      0.2.5                h7f98852_2    conda-forge
+yarl                      1.9.4            py39hd1e30aa_0    conda-forge
+zeromq                    4.3.4                h9c3ff4c_1    conda-forge
+zict                      3.0.0              pyhd8ed1ab_0    conda-forge
+zip                       3.0                  hd590300_3    conda-forge
+zipp                      3.7.0              pyhd8ed1ab_0    conda-forge
+zlib                      1.2.13               hd590300_5    conda-forge
+zstd                      1.5.5                hfc55251_0    conda-forge
+
+
+
+
+
+
+
import wradlib as wrl
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+
wrl.show_versions()
+
+
+
+
+
INSTALLED VERSIONS
+------------------
+commit: None
+python: 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:45:29) 
+[GCC 10.4.0]
+python-bits: 64
+OS: Linux
+OS-release: 6.2.0-1019-azure
+machine: x86_64
+processor: 
+byteorder: little
+LC_ALL: en_US.UTF-8
+LANG: en_US.UTF-8
+LOCALE: ('en_US', 'UTF-8')
+libhdf5: 1.12.2
+libnetcdf: 4.8.1
+
+xarray: 2022.6.0
+pandas: 1.4.3
+numpy: 1.23.2
+scipy: 1.9.1
+netCDF4: 1.6.2
+pydap: None
+h5netcdf: 1.3.0
+h5py: 3.7.0
+Nio: None
+zarr: None
+cftime: 1.6.3
+nc_time_axis: None
+PseudoNetCDF: None
+rasterio: 1.3.3
+cfgrib: None
+iris: None
+bottleneck: None
+dask: 2022.8.1
+distributed: 2022.8.1
+matplotlib: 3.5.3
+cartopy: 0.20.3
+seaborn: None
+numbagg: None
+fsspec: 2024.2.0
+cupy: None
+pint: None
+sparse: None
+flox: None
+numpy_groupies: None
+setuptools: 60.5.0
+pip: 22.2.2
+conda: None
+pytest: None
+IPython: 8.0.1
+sphinx: 4.5.0
+
+wradlib: 1.16.2
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.
+  warnings.warn("Setuptools is replacing distutils.")
+
+
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

ERAD 2022 Open Source Workshop

+
+ + +
+

next

+

Combining Radar Workflows

+
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+ + + +
+
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+ +
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+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
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+ Canada +
+
+ NCAR +
+
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+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/lrose/nexrad_mosaic.erad_tutorial.html b/_preview/104/notebooks/lrose/nexrad_mosaic.erad_tutorial.html new file mode 100644 index 00000000..d328c6c9 --- /dev/null +++ b/_preview/104/notebooks/lrose/nexrad_mosaic.erad_tutorial.html @@ -0,0 +1,9566 @@ + + + + + + + + ERAD 2022 Open Source Workshop — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

ERAD 2022 Open Source Workshop

+
+

LROSE workflow - combining 3 NEXRAD radars, computing PID and Echo Type

+../../_images/erad2022_cidd_mosaic.png +

The case we will use is from 2021/07/06 22:00 UTC, when a series of MCSs passed through the NW region of Kansas in the USA.

+

We will combine the reflectivity of 3 NEXRAD radars into a mini-mosaic:

+
    +

  • KGLD - Goodland, Kansas

    • +

  • KUEX - Hastings, Nebraska

    • +

  • KDDC - Dodge City, Kansas

    • +
+

We will download the following data sets from the cloud:

+
    +

  • raw NEXRAD files for KGLD, KUEX and KDDC, 2021/07/06 from 22:00 UTC to 22:30 UTC

    • +

  • RUC model output for Kansas region, CF-NetCDF format, from 2021/07/06 at 23:00 UTC

    • +
+

The model data will provide a temperature profile for computing PID and Echo Type.

+

The workflow is as follows:

+
    +

  • download the data into /tmp/lrose_data/nexrad_mosaic.

    • +

  • run LROSE app RadxConvert, to convert raw NEXRAD files to CfRadial.

    • +

  • plot an example PPI from KGLD using PyArt.

    • +

  • read in temperature data from RUC file, plot cross sections using Matplotlib.

    • +

  • run the LROSE app Mdv2SoundingSpdb to derive the temperature profile for each radar site from the RUC data file, and store in SPDB (a simple time-indexed data base).

    • +

  • run the LROSE app RadxRate to compute precipition rate and Particle ID (PID).

    • +

  • plot KDP, PID and precipitation rate for an example PPI.

    • +

  • run the LROSE app Radx2Grid to convert the polar CfRadial files into Cartesian coordinates.

    • +

  • run the LROSE app MdvMerge2 to merge the Cartesian files from the 3 radars into a reflectivity mini-mosaic.

    • +

  • plot selected views of the reflectivity mosaic, using Matplotlib.

    • +

  • run the LROSE app Ecco to compute the convective/stratiform partition using the reflectivity mosaic and the RUC temperature profile.

    • +

  • plot the results of Ecco using Matplotlib.

    • +
+

We will use the following parameter files for the LROSE applications:

+
    +

  • params/RadxConvert.nexrad - convert raw NEXRAD files to CfRadial.

    • +

  • params/Mdv2SoundingSpdb.ruc - create temperature profiles for each radar from RUC model temperature.

    • +

  • params/RadxRate.nexrad - computes KDP, PID and precipition rate.

    • +

  • params/kdp_params.nexrad - used by RadxRate to compute KDP.

    • +

  • params/pid_params.nexrad - used by RadxRate to compute PID.

    • +

  • params/pid_thresholds.nexrad - used by RadxRate to compute PID.

    • +

  • params/rate_params.nexrad - used by RadxRate to compute precipitation rate.

    • +

  • params/Ecco.nexrad_mosaic - used by Ecco to compute echo type classifications.

    • +
+

After the download step, the input files will be in:

+
  /tmp/lrose_data/nexrad_mosaic/raw/KGLD
+  /tmp/lrose_data/nexrad_mosaic/raw/KUEX
+  /tmp/lrose_data/nexrad_mosaic/raw/KDDC
+  /tmp/lrose_data/nexrad_mosaic/mdv/ruc
+
+
+

The output files will be stored in:

+
  /tmp/lrose_data/nexrad_mosaic/cfradial (polar data)
+  /tmp/lrose_data/nexrad_mosaic/mdv (Cartesian data)
+  /tmp/lrose_data/nexrad_mosaic/spdb (temperature profile per radar)
+
+
+
+
+

Initialize python

+
+
+
#
+# Extra packages to be added to anaconda3 standard packages for this notebook:
+#
+#  conda update --all
+#  conda install cartopy netCDF4
+#  conda install -c conda-forge arm_pyart
+#
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import os
+import datetime
+import pytz
+import math
+import numpy as np
+import matplotlib as mp
+import matplotlib.pyplot as plt
+import matplotlib.ticker as plticker
+from matplotlib.lines import Line2D
+import cartopy
+import cartopy.crs as ccrs
+import cartopy.io.shapereader as shpreader
+import cartopy.geodesic as cgds
+from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+from cartopy import feature as cfeature
+import shapely
+import netCDF4 as nc
+import pyart
+
+# Set data dir in environment variable
+os.environ['NEXRAD_DATA_DIR'] = '/tmp/lrose_data/nexrad_mosaic'
+nexradDataDir = os.environ['NEXRAD_DATA_DIR']
+print('====>> nexradDataDir: ', nexradDataDir)
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
====>> nexradDataDir:  /tmp/lrose_data/nexrad_mosaic
+
+
+
+
+
+
+

Download data sets from web

+
+
+
# Download input data sets
+#
+# 1. NEXRAD raw data files for KGLD, KDDC and KUEX
+# 2. RUC model data for temperature profile, Kansas and surroundings
+#
+# These will be put in:
+#  ${NEXRAD_DATA_DIR}
+#
+
+# ensure the data dir exists and is clean
+
+!/bin/rm -rf ${NEXRAD_DATA_DIR}
+!mkdir -p ${NEXRAD_DATA_DIR}
+
+# download the data from github
+
+print("====>> Downloading data tar files into dir: ", nexradDataDir)
+
+!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KGLD.20210706_220000.tgz 
+!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KDDC.20210706_220000.tgz 
+!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.KUEX.20210706_220000.tgz 
+!cd ${NEXRAD_DATA_DIR}; wget http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz 
+ 
+# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz
+# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KGLD.20210706_220000.tgz
+# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KDDC.20210706_220000.tgz
+# !cd ${NEXRAD_DATA_DIR}; wget https://raw.githubusercontent.com/NCAR/lrose-data-examples/master/notebooks/nexrad_mosaic/nexrad_mosaic.KUEX.20210706_220000.tgz
+
+# extract the data from the tar files
+
+print("====>> The following data files are unpacked in dir: ", nexradDataDir)
+!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.ruc.20210706_220000.tgz
+!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KGLD.20210706_220000.tgz
+!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KDDC.20210706_220000.tgz
+!cd ${NEXRAD_DATA_DIR}; tar xvfz nexrad_mosaic.KUEX.20210706_220000.tgz
+
+# clean up
+
+!cd ${NEXRAD_DATA_DIR}; /bin/rm -f *tgz
+
+
+
+
+
====>> Downloading data tar files into dir:  /tmp/lrose_data/nexrad_mosaic
+
+
+
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--2024-02-21 08:11:11--  http://front.eol.ucar.edu/data/notebooks/nexrad_mosaic/nexrad_mosaic.ruc.20210706_220000.tgz
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+Saving to: ‘nexrad_mosaic.ruc.20210706_220000.tgz’
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====>> The following data files are unpacked in dir:  /tmp/lrose_data/nexrad_mosaic
+
+
+
mdv/
+mdv/ruc/
+mdv/ruc/20210706/
+mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+
+
+
raw/KGLD/
+raw/KGLD/20210706/
+raw/KGLD/20210706/KGLD20210706_220003_V06
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raw/KGLD/20210706/KGLD20210706_220448_V06
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raw/KGLD/20210706/KGLD20210706_222834_V06
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raw/KDDC/
+raw/KDDC/20210706/
+raw/KDDC/20210706/KDDC20210706_220000_V06
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raw/KDDC/20210706/KDDC20210706_220430_V06
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raw/KDDC/20210706/KDDC20210706_220921_V06
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raw/KDDC/20210706/KDDC20210706_221600_V06
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raw/KDDC/20210706/KDDC20210706_222533_V06
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raw/KUEX/
+raw/KUEX/20210706/
+raw/KUEX/20210706/KUEX20210706_220249_V06
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raw/KUEX/20210706/KUEX20210706_220723_V06
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raw/KUEX/20210706/KUEX20210706_221204_V06
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raw/KUEX/20210706/KUEX20210706_221633_V06
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raw/KUEX/20210706/KUEX20210706_222102_V06
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raw/KUEX/20210706/KUEX20210706_222531_V06
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+
+
+
+

Notes on LROSE Parameter files

+

All LROSE applications have a detailed parameter file, which is read in at startup.

+

The parameters allow the user to control the processing in the LROSE apps.

+

To generate a default parameter file, you use the -print_params option for the app.

+

For example, for RadxConvert you would use:

+
  RadxConvert -print_params > RadxConvert.nexrad
+
+
+

and then edit RadxConvert.nexrad appropriately.

+

At runtime you would use:

+
  RadxConvert -params RadxConvert.nexrad ... etc ...
+
+
+
+
+

View the RadxConvert parameter file

+

Note that we can use environment variables in the parameter files.

+

Environment variables are inserted using the format:

+
  $(env_var_name)
+
+
+

For example:

+
  input_dir = "$(NEXRAD_DATA_DIR)/raw/$(RADAR_NAME)";
+
+
+
+
+
# View the param file
+!cat ./params/RadxConvert.nexrad
+
+
+
+
+
/**********************************************************************
+ * TDRP params for RadxConvert
+ **********************************************************************/
+
+//======================================================================
+//
+// Converts files between CfRadial and other radial formats.
+//
+//======================================================================
+ 
+//======================================================================
+//
+// DEBUGGING.
+//
+//======================================================================
+ 
+///////////// debug ///////////////////////////////////
+//
+// Debug option.
+// If set, debug messages will be printed appropriately.
+//
+// Type: enum
+// Options:
+//     DEBUG_OFF
+//     DEBUG_NORM
+//     DEBUG_VERBOSE
+//     DEBUG_EXTRA
+//
+
+debug = DEBUG_OFF;
+
+///////////// instance ////////////////////////////////
+//
+// Program instance for process registration.
+// This application registers with procmap. This is the instance used 
+//   for registration.
+// Type: string
+//
+
+instance = "$(RADAR_NAME)";
+
+//======================================================================
+//
+// DATA INPUT.
+//
+//======================================================================
+ 
+///////////// input_dir ///////////////////////////////
+//
+// Input directory for searching for files.
+// Files will be searched for in this directory.
+// Type: string
+//
+
+input_dir = "$(NEXRAD_DATA_DIR)/raw/$(RADAR_NAME)";
+
+///////////// mode ////////////////////////////////////
+//
+// Operating mode.
+// In REALTIME mode, the program waits for a new input file.  In ARCHIVE 
+//   mode, it moves through the data between the start and end times set 
+//   on the command line. In FILELIST mode, it moves through the list of 
+//   file names specified on the command line. Paths (in ARCHIVE mode, at 
+//   least) MUST contain a day-directory above the data file -- 
+//   ./data_file.ext will not work as a file path, but 
+//   ./yyyymmdd/data_file.ext will.
+//
+// Type: enum
+// Options:
+//     REALTIME
+//     ARCHIVE
+//     FILELIST
+//
+
+mode = ARCHIVE;
+
+///////////// max_realtime_data_age_secs //////////////
+//
+// Maximum age of realtime data (secs).
+// Only data less old than this will be used.
+// Type: int
+//
+
+max_realtime_data_age_secs = 300;
+
+///////////// latest_data_info_avail //////////////////
+//
+// Is _latest_data_info file available?.
+// If TRUE, will watch the latest_data_info file. If FALSE, will scan 
+//   the input directory for new files.
+// Type: boolean
+//
+
+latest_data_info_avail = FALSE;
+
+///////////// search_recursively //////////////////////
+//
+// Option to recurse to subdirectories while looking for new files.
+// If TRUE, all subdirectories with ages less than max_dir_age will be 
+//   searched. This may take considerable CPU, so be careful in its use. 
+//   Only applies if latest_data_info_avail is FALSE.
+// Type: boolean
+//
+
+search_recursively = TRUE;
+
+///////////// max_recursion_depth /////////////////////
+//
+// Maximum depth for recursive directory scan.
+// Only applies search_recursively is TRUE. This is the max depth, below 
+//   input_dir, to which the recursive directory search will be carried 
+//   out. A depth of 0 will search the top-level directory only. A depth 
+//   of 1 will search the level below the top directory, etc.
+// Type: int
+//
+
+max_recursion_depth = 5;
+
+///////////// wait_between_checks /////////////////////
+//
+// Sleep time between checking directory for input - secs.
+// If a directory is large and files do not arrive frequently, set this 
+//   to a higher value to reduce the CPU load from checking the directory. 
+//   Only applies if latest_data_info_avail is FALSE.
+// Minimum val: 1
+// Type: int
+//
+
+wait_between_checks = 2;
+
+///////////// file_quiescence /////////////////////////
+//
+// File quiescence when checking for files - secs.
+// This allows you to make sure that a file coming from a remote machine 
+//   is complete before reading it. Only applies if latest_data_info_avail 
+//   is FALSE.
+// Type: int
+//
+
+file_quiescence = 60;
+
+///////////// search_ext //////////////////////////////
+//
+// File name extension.
+// If set, only files with this extension will be processed.
+// Type: string
+//
+
+search_ext = "";
+
+///////////// gematronik_realtime_mode ////////////////
+//
+// Set to TRUE if we are watching for Gematronik XML volumes.
+// Gematronik volumes (for a given time) are stored in multiple files, 
+//   one for each field. Therefore, after the time on a volume changes and 
+//   a new field file is detected, we need to wait a while to ensure that 
+//   all of the files have had a chance to be writted to disk. You need to 
+//   set gematronik_realtime_wait_secs to a value in excess of the time it 
+//   takes for all of the files to be written.
+// Type: boolean
+//
+
+gematronik_realtime_mode = FALSE;
+
+///////////// gematronik_realtime_wait_secs ///////////
+//
+// Number of seconds to wait, so that all field files can be written to 
+//   disk before we start to read.
+// See 'gematronik_realtime_mode'.
+// Type: int
+//
+
+gematronik_realtime_wait_secs = 5;
+
+//======================================================================
+//
+// OPTIONAL FIXED ANGLE OR SWEEP NUMBER LIMITS.
+//
+// Fixed angles are elevation in PPI mode and azimuth in RHI mode.
+//
+//======================================================================
+ 
+///////////// set_fixed_angle_limits //////////////////
+//
+// Option to set fixed angle limits.
+// Only use sweeps within the specified fixed angle limits.
+// Type: boolean
+//
+
+set_fixed_angle_limits = FALSE;
+
+///////////// lower_fixed_angle_limit /////////////////
+//
+// Lower fixed angle limit - degrees.
+// Type: double
+//
+
+lower_fixed_angle_limit = 0;
+
+///////////// upper_fixed_angle_limit /////////////////
+//
+// Upper fixed angle limit - degrees.
+// Type: double
+//
+
+upper_fixed_angle_limit = 90;
+
+///////////// set_sweep_num_limits ////////////////////
+//
+// Option to set sweep number limits.
+// If 'apply_strict_angle_limits' is set, only read sweeps within the 
+//   specified limits. If strict checking is false and no data lies within 
+//   the limits, return the closest applicable sweep.
+// Type: boolean
+//
+
+set_sweep_num_limits = FALSE;
+
+///////////// lower_sweep_num /////////////////////////
+//
+// Lower sweep number limit.
+// Type: int
+//
+
+lower_sweep_num = 0;
+
+///////////// upper_sweep_num /////////////////////////
+//
+// Upper sweep number limit.
+// Type: int
+//
+
+upper_sweep_num = 0;
+
+///////////// apply_strict_angle_limits ///////////////
+//
+// Option to apply strict checking for angle or sweep number limits on 
+//   read.
+// If true, an error will occur if the fixed angle limits or sweep num 
+//   limits are outside the bounds of the data. If false, a read is 
+//   guaranteed to return at least 1 sweep - if no sweep lies within the 
+//   angle limits set, the nearest sweep will be returned.
+// Type: boolean
+//
+
+apply_strict_angle_limits = TRUE;
+
+///////////// read_set_radar_num //////////////////////
+//
+// Option to set the radar number.
+// See read_radar_num.
+// Type: boolean
+//
+
+read_set_radar_num = FALSE;
+
+///////////// read_radar_num //////////////////////////
+//
+// Set the radar number for the data to be extracted.
+// Most files have data from a single radar, so this does not apply. The 
+//   NOAA HRD files, however, have data from both the lower fuselage (LF, 
+//   radar_num = 1) and tail (TA, radar_num = 2) radars. For HRD files, by 
+//   default the TA radar will be used, unless the radar num is set to 1 
+//   for the LF radar.
+// Type: int
+//
+
+read_radar_num = 0;
+
+//======================================================================
+//
+// READ OPTIONS.
+//
+//======================================================================
+ 
+///////////// aggregate_sweep_files_on_read ///////////
+//
+// Option to aggregate sweep files into a volume on read.
+// If true, and the input data is in sweeps rather than volumes (e.g. 
+//   DORADE), the sweep files from a volume will be aggregated into a 
+//   volume.
+// Type: boolean
+//
+
+aggregate_sweep_files_on_read = FALSE;
+
+///////////// aggregate_all_files_on_read /////////////
+//
+// Option to aggregate all files in the file list on read.
+// If true, all of the files specified with the '-f' arg will be 
+//   aggregated into a single volume as they are read in. This only 
+//   applies to FILELIST mode. Overrides 'aggregate_sweep_files_on_read'.
+// Type: boolean
+//
+
+aggregate_all_files_on_read = FALSE;
+
+///////////// ignore_idle_scan_mode_on_read ///////////
+//
+// Option to ignore data taken in IDLE mode.
+// If true, on read will ignore files with an IDLE scan mode.
+// Type: boolean
+//
+
+ignore_idle_scan_mode_on_read = TRUE;
+
+///////////// remove_rays_with_all_data_missing ///////
+//
+// Option to remove rays for which all data is missing.
+// If true, ray data will be checked. If all fields have missing data at 
+//   all gates, the ray will be removed after reading.
+// Type: boolean
+//
+
+remove_rays_with_all_data_missing = FALSE;
+
+///////////// remove_rays_with_antenna_transitions ////
+//
+// Option to remove rays taken while the antenna was in transition.
+// If true, rays with the transition flag set will not be used. The 
+//   transiton flag is set when the antenna is in transtion between one 
+//   sweep and the next.
+// Type: boolean
+//
+
+remove_rays_with_antenna_transitions = FALSE;
+
+///////////// transition_nrays_margin /////////////////
+//
+// Number of transition rays to include as a margin.
+// Sometimes the transition flag is turned on too early in a transition, 
+//   on not turned off quickly enough after a transition. If you set this 
+//   to a number greater than 0, that number of rays will be included at 
+//   each end of the transition, i.e. the transition will effectively be 
+//   shorter at each end by this number of rays.
+// Type: int
+//
+
+transition_nrays_margin = 0;
+
+///////////// trim_surveillance_sweeps_to_360deg //////
+//
+// Option to trip surveillance sweeps so that they only cover 360 
+//   degrees.
+// Some sweeps will have rays which cover more than a 360-degree 
+//   rotation. Often these include antenna transitions. If this is set to 
+//   true, rays are trimmed off either end of the sweep to limit the 
+//   coverage to 360 degrees. The median elevation angle is computed and 
+//   the end ray which deviates from the median in elevation is trimmed 
+//   first.
+// Type: boolean
+//
+
+trim_surveillance_sweeps_to_360deg = FALSE;
+
+///////////// set_max_range ///////////////////////////
+//
+// Option to set the max range for any ray.
+// Type: boolean
+//
+
+set_max_range = TRUE;
+
+///////////// max_range_km ////////////////////////////
+//
+// Specified maximim range - km.
+// Gates beyond this range are removed.
+// Type: double
+//
+
+max_range_km = 230.0;
+
+///////////// preserve_sweeps /////////////////////////
+//
+// Preserve sweeps just as they are in the file.
+// Applies generally to NEXRAD data. If true, the sweep details are 
+//   preserved. If false, we consolidate sweeps from split cuts into a 
+//   single sweep.
+// Type: boolean
+//
+
+preserve_sweeps = FALSE;
+
+///////////// remove_long_range_rays //////////////////
+//
+// Option to remove long range rays.
+// Applies to NEXRAD data. If true, data from the non-Doppler long-range 
+//   sweeps will be removed.
+// Type: boolean
+//
+
+remove_long_range_rays = TRUE;
+
+///////////// remove_short_range_rays /////////////////
+//
+// Option to remove short range rays.
+// Applies to NEXRAD data. If true, data from the Doppler short-range 
+//   sweeps will be removed.
+// Type: boolean
+//
+
+remove_short_range_rays = FALSE;
+
+///////////// set_ngates_constant /////////////////////
+//
+// Option to force the number of gates to be constant.
+// If TRUE, the number of gates on all rays will be set to the maximum, 
+//   and gates added to shorter rays will be filled with missing values.
+// Type: boolean
+//
+
+set_ngates_constant = FALSE;
+
+//======================================================================
+//
+// OPTION TO OVERRIDE INSTRUMENT AND/OR NAME.
+//
+//======================================================================
+ 
+///////////// override_instrument_name ////////////////
+//
+// Option to override the instrument name.
+// If true, the name provided will be used.
+// Type: boolean
+//
+
+override_instrument_name = FALSE;
+
+///////////// instrument_name /////////////////////////
+//
+// Instrument name.
+// See override_instrument_name.
+// Type: string
+//
+
+instrument_name = "unknown";
+
+///////////// override_site_name //////////////////////
+//
+// Option to override the site name.
+// If true, the name provided will be used.
+// Type: boolean
+//
+
+override_site_name = FALSE;
+
+///////////// site_name ///////////////////////////////
+//
+// Site name.
+// See override_site_name.
+// Type: string
+//
+
+site_name = "unknown";
+
+//======================================================================
+//
+// OPTION TO OVERRIDE RADAR LOCATION.
+//
+//======================================================================
+ 
+///////////// override_radar_location /////////////////
+//
+// Option to override the radar location.
+// If true, the location in this file will be used. If not, the location 
+//   in the time series data will be used.
+// Type: boolean
+//
+
+override_radar_location = FALSE;
+
+///////////// radar_latitude_deg //////////////////////
+//
+// Radar latitude (deg).
+// See override_radar_location.
+// Type: double
+//
+
+radar_latitude_deg = -999;
+
+///////////// radar_longitude_deg /////////////////////
+//
+// Radar longitude (deg).
+// See override_radar_location.
+// Type: double
+//
+
+radar_longitude_deg = -999;
+
+///////////// radar_altitude_meters ///////////////////
+//
+// Radar altitude (meters).
+// See override_radar_location.
+// Type: double
+//
+
+radar_altitude_meters = -999;
+
+///////////// change_radar_latitude_sign //////////////
+//
+// Option to negate the latitude.
+// Mainly useful for RAPIC files. In RAPIC, latitude is always positive, 
+//   so mostly you need to set the latitiude to the negative value of 
+//   itself.
+// Type: boolean
+//
+
+change_radar_latitude_sign = FALSE;
+
+///////////// apply_georeference_corrections //////////
+//
+// Option to apply the georeference info for moving platforms.
+// For moving platforms, measured georeference information is sometimes 
+//   available. If this is set to true, the georeference data is applied 
+//   and appropriate corrections made. If possible, Earth-centric azimuth 
+//   and elevation angles will be computed.
+// Type: boolean
+//
+
+apply_georeference_corrections = FALSE;
+
+//======================================================================
+//
+// OPTION TO OVERRIDE SELECTED GLOBAL ATTRIBUTES.
+//
+//======================================================================
+ 
+///////////// version_override ////////////////////////
+//
+// Option to override the version global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the version attribute.
+// Type: string
+//
+
+version_override = "";
+
+///////////// title_override //////////////////////////
+//
+// Option to override the title global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the title attribute.
+// Type: string
+//
+
+title_override = "";
+
+///////////// institution_override ////////////////////
+//
+// Option to override the institution global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the institution attribute.
+// Type: string
+//
+
+institution_override = "";
+
+///////////// references_override /////////////////////
+//
+// Option to override the references global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the references attribute.
+// Type: string
+//
+
+references_override = "";
+
+///////////// source_override /////////////////////////
+//
+// Option to override the source global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the source attribute.
+// Type: string
+//
+
+source_override = "";
+
+///////////// history_override ////////////////////////
+//
+// Option to override the history global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the history attribute.
+// Type: string
+//
+
+history_override = "";
+
+///////////// comment_override ////////////////////////
+//
+// Option to override the comment global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the comment attribute.
+// Type: string
+//
+
+comment_override = "";
+
+///////////// author_override /////////////////////////
+//
+// Option to override the author global attribute.
+// If empty, no effect. If not empty, this string is used to override 
+//   the author attribute.
+// Type: string
+//
+
+author_override = "";
+
+//======================================================================
+//
+// OPTION TO CORRECT ANTENNA ANGLES.
+//
+//======================================================================
+ 
+///////////// apply_azimuth_offset ////////////////////
+//
+// Option to apply an offset to the azimuth values.
+// If TRUE, this offset will be ADDED to the measured azimuth angles. 
+//   This is useful, for example, in the case of a mobile platform which 
+//   is not set up oriented to true north. Suppose you have a truck (like 
+//   the DOWs) which is oriented off true north. Then if you add in the 
+//   truck HEADING relative to true north, the measured azimuth angles 
+//   will be adjusted by the heading, to give azimuth relative to TRUE 
+//   north.
+// Type: boolean
+//
+
+apply_azimuth_offset = FALSE;
+
+///////////// azimuth_offset //////////////////////////
+//
+// Azimuth offset (degrees).
+// See 'apply_azimuth_offset'. This value will be ADDED to the measured 
+//   azimuths.
+// Type: double
+//
+
+azimuth_offset = 0;
+
+///////////// apply_elevation_offset //////////////////
+//
+// Option to apply an offset to the elevation values.
+// If TRUE, this offset will be ADDED to the measured elevation angles. 
+//   This is useful to correct for a systematic bias in measured elevation 
+//   angles.
+// Type: boolean
+//
+
+apply_elevation_offset = FALSE;
+
+///////////// elevation_offset ////////////////////////
+//
+// Elevation offset (degrees).
+// See 'apply_elevation_offset'. This value will be ADDED to the 
+//   measured elevations.
+// Type: double
+//
+
+elevation_offset = 0;
+
+//======================================================================
+//
+// OPTION TO SPECIFY FIELD NAMES AND OUTPUT ENCODING.
+//
+//======================================================================
+ 
+///////////// set_output_fields ///////////////////////
+//
+// Set the field names and output encoding.
+// If false, all fields will be used.
+// Type: boolean
+//
+
+set_output_fields = TRUE;
+
+///////////// output_fields ///////////////////////////
+//
+// Output field details.
+// Set the details for the output fields. The output_field_name is the 
+//   ndtCDF variable name. Set the long name to a more descriptive name. 
+//   Set the standard name to the CF standard name for this field. If the 
+//   long name or standard name are empty, the existing names are used. If 
+//   SCALING_SPECIFIED, then the scale and offset is used.
+//
+// Type: struct
+//   typedef struct {
+//      string input_field_name;
+//      string output_field_name;
+//      string long_name;
+//      string standard_name;
+//      string output_units;
+//      output_encoding_t encoding;
+//        Options:
+//          OUTPUT_ENCODING_ASIS
+//          OUTPUT_ENCODING_FLOAT32
+//          OUTPUT_ENCODING_INT32
+//          OUTPUT_ENCODING_INT16
+//          OUTPUT_ENCODING_INT08
+//      output_scaling_t output_scaling;
+//        Options:
+//          SCALING_DYNAMIC
+//          SCALING_SPECIFIED
+//      double output_scale;
+//      double output_offset;
+//   }
+//
+// 1D array - variable length.
+//
+
+output_fields = {
+  {
+    input_field_name = "REF",
+    output_field_name = "DBZ",
+    long_name = "radar_reflectivity",
+    standard_name = "equivalent_reflectivity_factor",
+    output_units = "dBZ",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+  ,
+  {
+    input_field_name = "VEL",
+    output_field_name = "VEL",
+    long_name = "radial_velocity",
+    standard_name = "radial_velocity_of_scatterers_away_from_instrument",
+    output_units = "m/s",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+  ,
+  {
+    input_field_name = "SW",
+    output_field_name = "WIDTH",
+    long_name = "spectrum_width",
+    standard_name = "doppler_spectrum_width",
+    output_units = "m/s",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+  ,
+  {
+    input_field_name = "ZDR",
+    output_field_name = "ZDR",
+    long_name = "differential_reflectivity",
+    standard_name = "log_differential_reflectivity_hv",
+    output_units = "dB",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+  ,
+  {
+    input_field_name = "PHI",
+    output_field_name = "PHIDP",
+    long_name = "differential_phase",
+    standard_name = "differential_phase_hv",
+    output_units = "deg",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+  ,
+  {
+    input_field_name = "RHO",
+    output_field_name = "RHOHV",
+    long_name = "cross_correlation",
+    standard_name = "cross_correlation_ratio_hv",
+    output_units = "",
+    encoding = OUTPUT_ENCODING_INT16,
+    output_scaling = SCALING_DYNAMIC,
+    output_scale = 0.01,
+    output_offset = 0
+  }
+};
+
+///////////// write_other_fields_unchanged ////////////
+//
+// Option to write out the unspecified fields as they are.
+// If false, only the fields listed in output_fields will be written. If 
+//   this is true, all other fields will be written unchanged.
+// Type: boolean
+//
+
+write_other_fields_unchanged = FALSE;
+
+///////////// exclude_specified_fields ////////////////
+//
+// Option to exclude fields in the specified list.
+// If true, the specified fields will be excluded. This may be easier 
+//   than specifiying all of the fields to be included, if that list is 
+//   very long.
+// Type: boolean
+//
+
+exclude_specified_fields = FALSE;
+
+///////////// excluded_fields /////////////////////////
+//
+// List of fields to be excluded.
+// List the names to be excluded.
+// Type: string
+// 1D array - variable length.
+//
+
+excluded_fields = {
+ "DBZ",
+ "VEL"
+};
+
+//======================================================================
+//
+// OPTION TO SPECIFY OUTPUT ENCODING FOR ALL FIELDS.
+//
+//======================================================================
+ 
+///////////// set_output_encoding_for_all_fields //////
+//
+// Option to set output encoding for all fields.
+// Type: boolean
+//
+
+set_output_encoding_for_all_fields = FALSE;
+
+///////////// output_encoding /////////////////////////
+//
+// Output encoding for all fields, if requested.
+//
+// Type: enum
+// Options:
+//     OUTPUT_ENCODING_ASIS
+//     OUTPUT_ENCODING_FLOAT32
+//     OUTPUT_ENCODING_INT32
+//     OUTPUT_ENCODING_INT16
+//     OUTPUT_ENCODING_INT08
+//
+
+output_encoding = OUTPUT_ENCODING_ASIS;
+
+//======================================================================
+//
+// CENSORING.
+//
+// You have the option of censoring the data fields - i.e. setting the 
+//   fields to missing values - at gates which meet certain criteria. If 
+//   this is done correctly, it allows you to preserve the valid data and 
+//   discard the noise, thereby improving compression.
+//
+//======================================================================
+ 
+///////////// apply_censoring /////////////////////////
+//
+// Apply censoring based on field values and thresholds.
+// If TRUE, censoring will be performed. See 'censoring_fields' for 
+//   details on how the censoring is applied.
+// Type: boolean
+//
+
+apply_censoring = FALSE;
+
+///////////// censoring_fields ////////////////////////
+//
+// Fields to be used for censoring.
+// Specify the fields to be used to determine whether a gate should be 
+//   censored. The name refers to the input data field names. Valid field 
+//   values lie in the range from min_valid_value to max_valid_value 
+//   inclusive. If the value of a field at a gate lies within this range, 
+//   it is considered valid. Each specified field is examined at each 
+//   gate, and is flagged as valid if its value lies in the valid range. 
+//   These field flags are then combined as follows: first, all of the 
+//   LOGICAL_OR flags are combined, yielding a single combined_or flag 
+//   which is true if any of the LOGICAL_OR fields is true. The 
+//   combined_or flag is then combined with all of the LOGICAL_AND fields, 
+//   yielding a true value only if the combined_or flag and the 
+//   LOGICAL_AND fields are all true. If this final flag is true, then the 
+//   data at the gate is regarded as valid and is retained. If the final 
+//   flag is false, the data at the gate is censored, and all of the 
+//   fields at the gate are set to missing.
+//
+// Type: struct
+//   typedef struct {
+//      string name;
+//      double min_valid_value;
+//      double max_valid_value;
+//      logical_t combination_method;
+//        Options:
+//          LOGICAL_AND
+//          LOGICAL_OR
+//   }
+//
+// 1D array - variable length.
+//
+
+censoring_fields = {
+  {
+    name = "SNR",
+    min_valid_value = 0,
+    max_valid_value = 1000,
+    combination_method = LOGICAL_OR
+  }
+  ,
+  {
+    name = "NCP",
+    min_valid_value = 0.15,
+    max_valid_value = 1000,
+    combination_method = LOGICAL_OR
+  }
+};
+
+///////////// censoring_min_valid_run /////////////////
+//
+// Minimum valid run of non-censored gates.
+// Only active if set to 2 or greater. A check is made to remove short 
+//   runs of noise. Looking along the radial, we compute the number of 
+//   contiguous gates (a 'run') with uncensored data. For the gates in 
+//   this run to be accepted the length of the run must exceed 
+//   censoring_min_valid_run. If the number of gates in a run is less than 
+//   this, then all gates in the run are censored.
+// Type: int
+//
+
+censoring_min_valid_run = 1;
+
+//======================================================================
+//
+// OPTION TO APPLY LINEAR TRANSFORM TO SPECIFIED FIELDS.
+//
+// These transforms are fixed. The same transform is applied to all 
+//   files.
+//
+//======================================================================
+ 
+///////////// apply_linear_transforms /////////////////
+//
+// Apply linear transform to specified fields.
+// If true, we will apply a linear transform to selected fields.
+// Type: boolean
+//
+
+apply_linear_transforms = FALSE;
+
+///////////// transform_fields ////////////////////////
+//
+// transform field details.
+// Set the field name, scale and offset to be applied to the selected 
+//   fields. NOTE: the field name is the INPUT field name.
+//
+// Type: struct
+//   typedef struct {
+//      string input_field_name;
+//      double transform_scale;
+//      double transform_offset;
+//   }
+//
+// 1D array - variable length.
+//
+
+transform_fields = {
+  {
+    input_field_name = "DBZ",
+    transform_scale = 1,
+    transform_offset = 0
+  }
+  ,
+  {
+    input_field_name = "VEL",
+    transform_scale = 1,
+    transform_offset = 0
+  }
+};
+
+//======================================================================
+//
+// OPTION TO APPLY VARIABLE LINEAR TRANSFORM TO SPECIFIED FIELDS.
+//
+// These transforms vary from file to file, controlled by specific 
+//   metadata.
+//
+//======================================================================
+ 
+///////////// apply_variable_transforms ///////////////
+//
+// Apply linear transforms that vary based on specific metadata.
+// If true, we will apply variable linear transform to selected fields.
+// Type: boolean
+//
+
+apply_variable_transforms = FALSE;
+
+///////////// variable_transform_fields ///////////////
+//
+// Details for variable transforms.
+// We based the field decision off the input_field_name. You need to 
+//   pick the method of control: STATUS_XML_FIELD - based on the value 
+//   associated with an XML tag in the status block; ELEVATION_DEG - based 
+//   on the elevation in degrees; PULSE_WIDTH_US - based on the pulse with 
+//   in microsecs. For STATUS_XML_FIELD Set the relevant status_xml_tag, 
+//   which will be used to find the relevant value. The lookup table is a 
+//   series of entries specifying the metadata_value and the scale and 
+//   offset to be appied for that given metadata value. Each entry is 
+//   enclosed in parentheses, and is of the form (metadata_value, scale, 
+//   offset). The entries themselves are also are comma-separated. 
+//   Interpolation is used for metadata values that lie between those 
+//   specified in the lookup table. The enries in the lookup table should 
+//   have metadata_values that are monotonically increasing.
+//
+// Type: struct
+//   typedef struct {
+//      string input_field_name;
+//      variable_transform_control_t control;
+//        Options:
+//          STATUS_XML_FIELD
+//      string xml_tag;
+//      string lookup_table;
+//   }
+//
+// 1D array - variable length.
+//
+
+variable_transform_fields = {
+  {
+    input_field_name = "dBZ",
+    control = STATUS_XML_FIELD,
+    xml_tag = "gdrxanctxfreq",
+    lookup_table = "(57.0, 1.0, -0.7), (60.0, 1.0, -0.2), (64.0, 1.0, -0.3), (67.0, 1.0, -1.8), (68.0, 1.0, -1.2), (69.0, 1.0, -1.3)"
+  }
+  ,
+  {
+    input_field_name = "dBZv",
+    control = STATUS_XML_FIELD,
+    xml_tag = "gdrxanctxfreq",
+    lookup_table = "(57.0, 1.0, 0.1), (58.0, 1.0, 0.3), (60.0, 1.0, -0.3), (67.0, 1.0, -2.3), (69.0, 1.0, -2.0)"
+  }
+  ,
+  {
+    input_field_name = "ZDR",
+    control = STATUS_XML_FIELD,
+    xml_tag = "gdrxanctxfreq",
+    lookup_table = "(56.0, 1.0, -0.75), (58.0, 1.0, -0.75), (61.0, 1.0, 0.1), (63.5, 1.0, 0.2), (64.0, 1.0, 0.6), (69.0, 1.0, 0.6)"
+  }
+};
+
+//======================================================================
+//
+// OUTPUT FORMAT.
+//
+//======================================================================
+ 
+///////////// output_format ///////////////////////////
+//
+// Format for the output files.
+//
+// Type: enum
+// Options:
+//     OUTPUT_FORMAT_CFRADIAL
+//     OUTPUT_FORMAT_DORADE
+//     OUTPUT_FORMAT_FORAY
+//     OUTPUT_FORMAT_NEXRAD
+//     OUTPUT_FORMAT_UF
+//     OUTPUT_FORMAT_MDV_RADIAL
+//
+
+output_format = OUTPUT_FORMAT_CFRADIAL;
+
+///////////// netcdf_style ////////////////////////////
+//
+// NetCDF style - if output_format is CFRADIAL.
+// netCDF classic format, netCDF 64-bit offset format, netCDF4 using 
+//   HDF5 format, netCDF4 using HDF5 format but only netCDF3 calls.
+//
+// Type: enum
+// Options:
+//     CLASSIC
+//     NC64BIT
+//     NETCDF4
+//     NETCDF4_CLASSIC
+//
+
+netcdf_style = NETCDF4;
+
+//======================================================================
+//
+// OUTPUT BYTE-SWAPPING and COMPRESSION.
+//
+//======================================================================
+ 
+///////////// output_native_byte_order ////////////////
+//
+// Option to leave data in native byte order.
+// If false, data will be byte-swapped as appropriate on output.
+// Type: boolean
+//
+
+output_native_byte_order = FALSE;
+
+///////////// output_compressed ///////////////////////
+//
+// Option to compress data fields on output.
+// Applies to netCDF and Dorade. UF does not support compression.
+// Type: boolean
+//
+
+output_compressed = TRUE;
+
+//======================================================================
+//
+// OUTPUT OPTIONS FOR CfRadial FILES.
+//
+//======================================================================
+ 
+///////////// output_force_ngates_vary ////////////////
+//
+// Option to force the use of ragged arrays for CfRadial files.
+// Only applies to CfRadial. If true, forces the use of ragged arrays 
+//   even if the number of gates for all rays is constant.
+// Type: boolean
+//
+
+output_force_ngates_vary = FALSE;
+
+///////////// compression_level ///////////////////////
+//
+// Compression level for output, if compressed.
+// Applies to netCDF only. Dorade compression is run-length encoding, 
+//   and has not options..
+// Type: int
+//
+
+compression_level = 4;
+
+//======================================================================
+//
+// OUTPUT DIRECTORY AND FILE NAME.
+//
+//======================================================================
+ 
+///////////// output_dir //////////////////////////////
+//
+// Output directory path.
+// Files will be written to this directory.
+// Type: string
+//
+
+output_dir = "$(NEXRAD_DATA_DIR)/cfradial/moments/$(RADAR_NAME)";
+
+///////////// output_filename_mode ////////////////////
+//
+// Mode for computing output file name.
+// START_AND_END_TIMES: include both start and end times in file name. 
+//   START_TIME_ONLY: include only start time in file name. END_TIME_ONLY: 
+//   include only end time in file name. SPECIFY_FILE_NAME: file of this 
+//   name will be written to output_dir.
+//
+// Type: enum
+// Options:
+//     START_AND_END_TIMES
+//     START_TIME_ONLY
+//     END_TIME_ONLY
+//     SPECIFY_FILE_NAME
+//
+
+output_filename_mode = START_AND_END_TIMES;
+
+///////////// output_filename_prefix //////////////////
+//
+// Optional prefix for output filename.
+// If empty, the standard prefix will be used.
+// Type: string
+//
+
+output_filename_prefix = "";
+
+///////////// include_instrument_name_in_file_name ////
+//
+// Option to include the instrument name in the file name.
+// Default is true. Only applies to CfRadial files. If true, the 
+//   instrument name will be included just before the volume number in the 
+//   output file name.
+// Type: boolean
+//
+
+include_instrument_name_in_file_name = TRUE;
+
+///////////// include_subsecs_in_file_name ////////////
+//
+// Option to include sub-seconds in date-time part of file name.
+// Default is true. Only applies to CfRadial files. If true, the 
+//   millisecs of the start and end time will be included in the file 
+//   name.
+// Type: boolean
+//
+
+include_subsecs_in_file_name = TRUE;
+
+///////////// use_hyphen_in_file_name_datetime_part ///
+//
+// Option to use a hyphen between date and time in filename.
+// Default is false. Only applies to CfRadial files. Normally an 
+//   underscore is used.
+// Type: boolean
+//
+
+use_hyphen_in_file_name_datetime_part = FALSE;
+
+///////////// output_filename /////////////////////////
+//
+// Name of output file.
+// Applies only if output_filename_mode is SPECIFY_FILE_NAME. File of 
+//   this name will be written to output_dir.
+// Type: string
+//
+
+output_filename = "cfradial.test.nc";
+
+///////////// append_day_dir_to_output_dir ////////////
+//
+// Add the day directory to the output directory.
+// Path will be output_dir/yyyymmdd/filename.
+// Type: boolean
+//
+
+append_day_dir_to_output_dir = TRUE;
+
+///////////// append_year_dir_to_output_dir ///////////
+//
+// Add the year directory to the output directory.
+// Path will be output_dir/yyyy/yyyymmdd/filename.
+// Type: boolean
+//
+
+append_year_dir_to_output_dir = FALSE;
+
+///////////// write_individual_sweeps /////////////////
+//
+// Option to write out individual sweeps if appropriate.
+// If true, the volume is split into individual sweeps for writing. 
+//   Applies to CfRadial format. This is always true for DORADE format 
+//   files.
+// Type: boolean
+//
+
+write_individual_sweeps = FALSE;
+
+///////////// write_latest_data_info //////////////////
+//
+// Option to write out _latest_data_info files.
+// If true, the _latest_data_info files will be written after the 
+//   converted file is written.
+// Type: boolean
+//
+
+write_latest_data_info = TRUE;
+
+//======================================================================
+//
+// OPTION TO OVERRIDE MISSING VALUES.
+//
+// Missing values are applicable to both metadata and field data. The 
+//   default values should be satisfactory for most purposes. However, you 
+//   can choose to override these if you are careful with the selected 
+//   values.
+
+// The default values for metadata are:
+// 	missingMetaDouble = -9999.0
+// 	missingMetaFloat = -9999.0
+// 	missingMetaInt = -9999
+// 	missingMetaChar = -128
+
+// The default values for field data are:
+// 	missingFl64 = -9.0e33
+// 	missingFl32 = -9.0e33
+// 	missingSi32 = -2147483647
+// 	missingSi16 = -32768
+// 	missingSi08 = -128.
+//
+//======================================================================
+ 
+///////////// override_missing_metadata_values ////////
+//
+// Option to override the missing values for meta-data.
+// See following parameter options.
+// Type: boolean
+//
+
+override_missing_metadata_values = FALSE;
+
+///////////// missing_metadata_double /////////////////
+//
+// Missing value for metadata of type double.
+// Only applies if override_missing_metadata_values is TRUE.
+// Type: double
+//
+
+missing_metadata_double = -9999;
+
+///////////// missing_metadata_float //////////////////
+//
+// Missing value for metadata of type float.
+// Only applies if override_missing_metadata_values is TRUE.
+// Type: float
+//
+
+missing_metadata_float = -9999;
+
+///////////// missing_metadata_int ////////////////////
+//
+// Missing value for metadata of type int.
+// Only applies if override_missing_metadata_values is TRUE.
+// Type: int
+//
+
+missing_metadata_int = -9999;
+
+///////////// missing_metadata_char ///////////////////
+//
+// Missing value for metadata of type char.
+// Only applies if override_missing_metadata_values is TRUE.
+// Type: int
+//
+
+missing_metadata_char = -128;
+
+///////////// override_missing_field_values ///////////
+//
+// Option to override the missing values for field data.
+// See following parameter options.
+// Type: boolean
+//
+
+override_missing_field_values = FALSE;
+
+///////////// missing_field_fl64 //////////////////////
+//
+// Missing value for field data of type 64-bit float.
+// Only applies if override_missing_field_values is TRUE.
+// Type: double
+//
+
+missing_field_fl64 = -9999;
+
+///////////// missing_field_fl32 //////////////////////
+//
+// Missing value for field data of type 32-bit float.
+// Only applies if override_missing_field_values is TRUE.
+// Type: double
+//
+
+missing_field_fl32 = -9999;
+
+///////////// missing_field_si32 //////////////////////
+//
+// Missing value for field data of type 32-bit integer.
+// Only applies if override_missing_field_values is TRUE.
+// Type: int
+//
+
+missing_field_si32 = -999999;
+
+///////////// missing_field_si16 //////////////////////
+//
+// Missing value for field data of type 16-bit integer.
+// Only applies if override_missing_field_values is TRUE.
+// Type: int
+//
+
+missing_field_si16 = -232768;
+
+///////////// missing_field_si08 //////////////////////
+//
+// Missing value for field data of type 8-bit integer.
+// Only applies if override_missing_field_values is TRUE.
+// Type: int
+//
+
+missing_field_si08 = -128;
+
+
+
+
+
+
+

Convert raw nexrad files to CfRadial format

+
+
+
# Convert raw nexrad data to cfradial for 3 NEXRAD radars
+
+for radar_name in ['KGLD', 'KUEX', 'KDDC']:
+    # Set radar in name environment variable
+    os.environ['RADAR_NAME'] = radar_name
+    # Run RadxConvert using param file
+    !/usr/local/lrose/bin/RadxConvert -params ./params/RadxConvert.nexrad -debug -start "2021 07 06 22 00 00" -end "2021 07 06 22 30 00"
+
+
+
+
+
======================================================================
+Program 'RadxConvert'
+Run-time 2024/02/21 08:11:17.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220003_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220003_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503080.713092.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503080.713092.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220448_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220448_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503085.330614.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503085.330614.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220935_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_220935_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503089.935551.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503089.935551.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221420_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221420_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503094.524454.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503094.524454.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221906_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_221906_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503099.129098.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503099.129098.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222350_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222350_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503103.715878.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503103.715878.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222834_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KGLD/20210706/KGLD20210706_222834_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503108.324214.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/tmp.701.1708503108.324214.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+
+
+
======================================================================
+Program 'RadxConvert'
+Run-time 2024/02/21 08:11:49.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220249_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220249_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503113.407460.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503113.407460.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220723_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_220723_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503118.41112.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503118.41112.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221204_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221204_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503122.611306.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503122.611306.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221633_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_221633_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503127.167292.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503127.167292.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222102_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222102_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503131.729951.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503131.729951.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222531_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KUEX/20210706/KUEX20210706_222531_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503136.272696.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/tmp.702.1708503136.272696.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+
+
+
======================================================================
+Program 'RadxConvert'
+Run-time 2024/02/21 08:12:17.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220000_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220000_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503141.642015.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503141.642015.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220430_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220430_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503146.713963.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503146.713963.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220921_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_220921_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503151.709146.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503151.709146.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_221600_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_221600_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503156.778033.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503156.778033.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222051_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222051_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503161.856860.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503161.856860.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+
+
+
INFO - RadxConvert::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222533_V06
+
+
+
WARNING - NexradRadxFile::readFromPath
+  Adaptation data probably not set, ignoring
+  File: /tmp/lrose_data/nexrad_mosaic/raw/KDDC/20210706/KDDC20210706_222533_V06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503166.937539.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: DBZ
+
+
+
  ... writing field: VEL
+
+
+
  ... writing field: WIDTH
+
+
+
  ... writing field: ZDR
+
+
+
  ... writing field: PHIDP
+
+
+
  ... writing field: RHOHV
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/tmp.703.1708503166.937539.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+
+
+
+
+
+
+

List the CfRadial files created by RadxConvert

+
+
+
# List the CfRadial files created by RadxConvert
+!ls -R ${NEXRAD_DATA_DIR}/cfradial/moments/K*/20*
+
+
+
+
+
/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706:
+cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+
+/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706:
+cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+
+/tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706:
+cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+
+
+
+
+
+
+

Plot one example of the NEXRAD Goodland radar (KGLD) CfRadial files

+
+
+
# Read CfRadial file into radar object
+filePathRadar = os.path.join(nexradDataDir, "cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc")
+radar_kgld = pyart.io.read_cfradial(filePathRadar)
+radar_kgld.info('compact')
+
+
+
+
+
altitude: <ndarray of type: float64 and shape: (1,)>
+altitude_agl: <ndarray of type: float64 and shape: (1,)>
+antenna_transition: <ndarray of type: int8 and shape: (6120,)>
+azimuth: <ndarray of type: float32 and shape: (6120,)>
+elevation: <ndarray of type: float32 and shape: (6120,)>
+fields:
+	DBZ: <ndarray of type: float32 and shape: (6120, 912)>
+	VEL: <ndarray of type: float32 and shape: (6120, 912)>
+	WIDTH: <ndarray of type: float32 and shape: (6120, 912)>
+	ZDR: <ndarray of type: float32 and shape: (6120, 912)>
+	PHIDP: <ndarray of type: float32 and shape: (6120, 912)>
+	RHOHV: <ndarray of type: float32 and shape: (6120, 912)>
+fixed_angle: <ndarray of type: float32 and shape: (14,)>
+instrument_parameters:
+	follow_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	pulse_width: <ndarray of type: float32 and shape: (6120,)>
+	prt_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	prt: <ndarray of type: float32 and shape: (6120,)>
+	prt_ratio: <ndarray of type: float32 and shape: (6120,)>
+	polarization_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	nyquist_velocity: <ndarray of type: float32 and shape: (6120,)>
+	unambiguous_range: <ndarray of type: float32 and shape: (6120,)>
+	n_samples: <ndarray of type: int32 and shape: (6120,)>
+	radar_antenna_gain_h: <ndarray of type: float32 and shape: (1,)>
+	radar_antenna_gain_v: <ndarray of type: float32 and shape: (1,)>
+	radar_beam_width_h: <ndarray of type: float32 and shape: (1,)>
+	radar_beam_width_v: <ndarray of type: float32 and shape: (1,)>
+	radar_rx_bandwidth: <ndarray of type: float32 and shape: (1,)>
+	measured_transmit_power_v: <ndarray of type: float32 and shape: (6120,)>
+	measured_transmit_power_h: <ndarray of type: float32 and shape: (6120,)>
+latitude: <ndarray of type: float64 and shape: (1,)>
+longitude: <ndarray of type: float64 and shape: (1,)>
+nsweeps: 14
+ngates: 912
+nrays: 6120
+radar_calibration:
+	r_calib_time: <ndarray of type: |S1 and shape: (1, 32)>
+	r_calib_pulse_width: <ndarray of type: float32 and shape: (1,)>
+	r_calib_xmit_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_xmit_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_waveguide_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_waveguide_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_radome_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_radome_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_mismatch_loss: <ndarray of type: float32 and shape: (1,)>
+	r_calib_k_squared_water: <ndarray of type: float32 and shape: (1,)>
+	r_calib_radar_constant_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_radar_constant_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_antenna_gain_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_antenna_gain_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_source_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_source_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_power_measure_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_power_measure_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_coupler_forward_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_coupler_forward_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dbz_correction: <ndarray of type: float32 and shape: (1,)>
+	r_calib_zdr_correction: <ndarray of type: float32 and shape: (1,)>
+	r_calib_ldr_correction_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_ldr_correction_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_system_phidp: <ndarray of type: float32 and shape: (1,)>
+	r_calib_test_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_test_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_index: <ndarray of type: int32 and shape: (6120,)>
+range: <ndarray of type: float32 and shape: (912,)>
+scan_rate: <ndarray of type: float32 and shape: (6120,)>
+scan_type: other
+sweep_end_ray_index: <ndarray of type: int32 and shape: (14,)>
+sweep_mode: <ndarray of type: |S1 and shape: (14, 32)>
+sweep_number: <ndarray of type: int32 and shape: (14,)>
+sweep_start_ray_index: <ndarray of type: int32 and shape: (14,)>
+target_scan_rate: <ndarray of type: float32 and shape: (14,)>
+time: <ndarray of type: float64 and shape: (6120,)>
+metadata:
+	Conventions: CF-1.7
+	Sub_conventions: CF-Radial instrument_parameters radar_parameters radar_calibration
+	version: CF-Radial-1.4
+	title: 
+	institution: 
+	references: 
+	source: ARCHIVE 2 data
+	history: 
+	comment: 
+	original_format: NEXRAD
+	driver: RadxConvert(NCAR)
+	created: 2024/02/21 08:11:20.711
+	start_datetime: 2021-07-06T22:00:03Z
+	time_coverage_start: 2021-07-06T22:00:03Z
+	start_time: 2021-07-06 22:00:03.963
+	end_datetime: 2021-07-06T22:04:39Z
+	time_coverage_end: 2021-07-06T22:04:39Z
+	end_time: 2021-07-06 22:04:39.770
+	instrument_name: KGLD
+	site_name: DLGK
+	scan_name: Surveillance
+	scan_id: 212
+	platform_is_mobile: false
+	n_gates_vary: false
+	ray_times_increase: true
+	volume_number: 79
+	platform_type: fixed
+	instrument_type: radar
+	primary_axis: axis_z
+
+
+
+
+
+
+
# Plot KGLD fields from this CfRadial file
+
+displayKgld = pyart.graph.RadarDisplay(radar_kgld)
+figKgld = plt.figure(1, (12, 10))
+
+# DBZ
+
+axDbz = figKgld.add_subplot(221)
+displayKgld.plot_ppi('DBZ', 0, vmin=-32, vmax=64.,
+                    axislabels=("x(km)", "y(km)"),
+                    colorbar_label="DBZ")
+displayKgld.plot_range_rings([50, 100, 150, 200])
+displayKgld.plot_cross_hair(200.)
+
+# VEL
+
+axVel = figKgld.add_subplot(222)
+displayKgld.plot_ppi('VEL', 0, vmin=-30, vmax=30.,
+                    axislabels=("x(km)", "y(km)"),
+                    colorbar_label="VEL(m/s)",
+                    cmap = "rainbow")
+displayKgld.plot_range_rings([50, 100, 150, 200])
+displayKgld.plot_cross_hair(200.)
+
+# ZDR
+
+axZdr = figKgld.add_subplot(223)
+displayKgld.plot_ppi('ZDR', 0, vmin=-4, vmax=16.,
+                    axislabels=("x(km)", "y(km)"),
+                    colorbar_label="ZDR(dB)",
+                    cmap = "rainbow")
+displayKgld.plot_range_rings([50, 100, 150, 200])
+displayKgld.plot_cross_hair(200.)
+
+# PHIDP
+
+axPhidp = figKgld.add_subplot(224)
+displayKgld.plot_ppi('PHIDP', 0,
+                    axislabels=("x(km)", "y(km)"),
+                    colorbar_label="PHIDP(deg)",
+                    cmap = "rainbow")
+displayKgld.plot_range_rings([50, 100, 150, 200])
+displayKgld.plot_cross_hair(200.)
+
+# set layout and display
+
+figKgld.tight_layout()
+plt.show()
+
+
+
+
+../../_images/nexrad_mosaic.erad_tutorial_14_0.png +
+
+
+

Read in temperature field from RUC model, to provide temperature profile for PID and Ecco

+
+
+
filePathModel = os.path.join(nexradDataDir, 'mdv/ruc/20210706/20210706_230000.mdv.cf.nc')
+dsModel = nc.Dataset(filePathModel)
+print(dsModel)
+dstemp = dsModel['TMP']
+temp3D = np.array(dstemp)
+fillValueTemp = dstemp._FillValue
+if (len(temp3D.shape) == 4):
+    temp3D = temp3D[0]
+
+# Compute time
+
+uTimeSecsModel = dsModel['start_time'][0]
+startTimeModel = datetime.datetime.fromtimestamp(int(uTimeSecsModel))
+startTimeStrModel = startTimeModel.strftime('%Y/%m/%d-%H:%M:%S UTC')
+print("Start time model: ", startTimeStrModel)
+
+# Compute Model grid limits
+(nZModel, nYModel, nXModel) = temp3D.shape
+lon = np.array(dsModel['x0'])
+lat = np.array(dsModel['y0'])
+ht = np.array(dsModel['z0'])
+dLonModel = lon[1] - lon[0]
+dLatModel = lat[1] - lat[0]
+minLonModel = lon[0] - dLonModel / 2.0
+maxLonModel = lon[-1] + dLonModel / 2.0
+minLatModel = lat[0] - dLatModel / 2.0
+maxLatModel = lat[-1] + dLatModel / 2.0
+minHtModel = ht[0]
+maxHtModel = ht[-1]
+print("nZModel, nYModel, nXModel", nZModel, nYModel, nXModel)
+print("minLonModel, maxLonModel: ", minLonModel, maxLonModel)
+print("minLatModel, maxLatModel: ", minLatModel, maxLatModel)
+print("minHt, maxHt: ", minHtModel, maxHtModel)
+print("Model hts: ", ht)
+del lon, lat, ht
+
+
+
+
+
<class 'netCDF4._netCDF4.Dataset'>
+root group (NETCDF4 data model, file format HDF5):
+    Conventions: CF-1.6
+    history: Converted from NetCDF to MDV, 2022/08/21 03:33:17
+  Ncf:comment: 
+
+    source: Grib2
+    title: RUC Rapid Refresh
+    comment: 
+    dimensions(sizes): time(1), bounds(2), x0(139), y0(72), z0(32), nbytes_mdv_chunk_0000(174)
+    variables(dimensions): float64 time(time), float64 forecast_reference_time(time), float64 forecast_period(time), float64 start_time(time), float64 stop_time(time), float32 x0(x0), float32 y0(y0), float32 z0(z0), int32 grid_mapping_0(), int32 mdv_master_header(time), int8 mdv_chunk_0000(time, nbytes_mdv_chunk_0000), float32 TMP(time, z0, y0, x0), float32 RH(time, z0, y0, x0), float32 UGRD(time, z0, y0, x0), float32 VGRD(time, z0, y0, x0), float32 VVEL(time, z0, y0, x0), float32 HGT(time, z0, y0, x0), float32 Pressure(time, z0, y0, x0)
+    groups: 
+Start time model:  2021/07/06-23:00:00 UTC
+nZModel, nYModel, nXModel 32 72 139
+minLonModel, maxLonModel:  -110.42499923706055 -89.57499313354492
+minLatModel, maxLatModel:  34.625 45.42500305175781
+minHt, maxHt:  1.2202008 16.17987
+Model hts:  [ 1.2202008  1.4573147  1.7001446  1.9490081  2.2042506  2.4662497
+  2.7354188  3.0122116  3.2971282  3.5907218  3.893606   4.2064652
+  4.530064   4.865264   5.213037   5.574489   5.950885   6.343679
+  6.75456    7.185502   7.6388354  8.117341   8.624373   9.164038
+  9.741439  10.363035  11.037209  11.784151  12.630965  13.60854
+ 14.764765  16.17987  ]
+
+
+
+
+
+
+
+

Plot W-E and N-S vertical sections of temperature data

+
+
+
# Compute Temp N-S vertical section
+nXHalfModel = int(nXModel/2)
+tempVertNS = temp3D[:, :, nXHalfModel:(nXHalfModel+1)]
+tempVertNS = tempVertNS.reshape(tempVertNS.shape[0], tempVertNS.shape[1])
+tempVertNS[tempVertNS == fillValueTemp] = np.nan
+print(tempVertNS.shape)
+tempNSMax = np.amax(temp3D, (2))
+tempNSMax[tempNSMax == fillValueTemp] = np.nan
+
+# Compute Temp W-E vertical section
+nYHalfModel = int(nYModel/2)
+tempVertWE = temp3D[:, nYHalfModel:(nYHalfModel+1), :]
+print(tempVertWE.shape)
+tempVertWE = tempVertWE.reshape(tempVertWE.shape[0], tempVertWE.shape[2])
+tempVertWE[tempVertWE == fillValueTemp] = np.nan
+tempWEMax = np.amax(temp3D, (1))
+tempWEMax[tempWEMax == fillValueTemp] = np.nan
+print(tempVertWE.shape)
+
+
+
+
+
(32, 72)
+(32, 1, 139)
+(32, 139)
+
+
+
+
+
+
+
# Plot model temp vertical sections at mid-points of grid
+
+figModelTemp = plt.figure(num=1, figsize=[12, 8], layout='constrained')
+
+# Plot W-E temp
+
+axWETemp = figModelTemp.add_subplot(1, 2, 1,
+                                    xlim = (minLonModel, maxLonModel),
+                                    ylim = (minHtModel, maxHtModel))
+plt.imshow(tempVertWE,
+    cmap='jet',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLonModel, maxLonModel, minHtModel, maxHtModel))
+axWETemp.set_aspect(1.0)
+axWETemp.set_xlabel('Longitude (deg)')
+axWETemp.set_ylabel('Height (km)')
+plt.colorbar(label="Temperature (C)", orientation="horizontal", fraction=0.1)
+plt.title("Vert slice mid W-E temp: " + startTimeStrModel)
+
+# Plot N-S temp vertical section
+
+axNSTemp = figModelTemp.add_subplot(1, 2, 2, 
+                                    xlim = (minLatModel, maxLatModel),
+                                    ylim = (minHtModel, maxHtModel))
+plt.imshow(tempVertNS,
+    cmap='jet',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLatModel, maxLatModel, minHtModel, maxHtModel))
+axNSTemp.set_aspect(0.52)
+axNSTemp.set_xlabel('Latitude (deg)')
+axNSTemp.set_ylabel('Height (km)')
+plt.colorbar(label="Temperature (C)", orientation="horizontal", fraction=0.1)
+plt.title("Vert slice mid N-S temp: " + startTimeStrModel)
+
+
+
+
+
Text(0.5, 1.0, 'Vert slice mid N-S temp: 2021/07/06-23:00:00 UTC')
+
+
+../../_images/nexrad_mosaic.erad_tutorial_19_1.png +
+
+
+
+

Sample RUC temperatures at 3 radar locations, save in SPDB data base

+
+
+
# Run Mdv2SoundingSpdb to sample temperature data and store in SPDB
+!/usr/local/lrose/bin/Mdv2SoundingSpdb -debug -params params/Mdv2SoundingSpdb.ruc -start "2021 07 06 00 00 00" -end "2021 07 07 00 00 00"
+
+
+
+
+
======================================================================
+Program 'Mdv2SoundingSpdb'
+Run-time 2024/02/21 08:12:51.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+Processing file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+
+
+
SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+SUCCESS - setting master header
+Default time dimension: time
+  time: 2021/07/06 23:00:00
+SUCCESS - setting time coord variable
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: TMP
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: TMP
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: TMP
+Adding data field: TMP
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::_shouldAddField
+  -->> adding field: RH
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: RH
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: RH
+Adding data field: RH
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: UGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: UGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: UGRD
+Adding data field: UGRD
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VGRD
+Adding data field: VGRD
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VVEL
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VVEL
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VVEL
+Adding data field: VVEL
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::_shouldAddField
+  -->> adding field: HGT
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: HGT
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: HGT
+Adding data field: HGT
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: Pressure
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: Pressure
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: Pressure
+Adding data field: Pressure
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::addDataFieldsTime elapsed = 0
+Adding chunk: NetCDF file global attributes
+Ncf2MdvTrans::addGlobalAttrXmlChunk()
+Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc
+       Station name : KGLD
+       Sounding time: 2021/07/06 23:00:00
+Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+
+
+
SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+SUCCESS - setting master header
+Default time dimension: time
+  time: 2021/07/06 23:00:00
+SUCCESS - setting time coord variable
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: TMP
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: TMP
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: TMP
+Adding data field: TMP
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::_shouldAddField
+  -->> adding field: RH
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: RH
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: RH
+Adding data field: RH
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: UGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: UGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: UGRD
+Adding data field: UGRD
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VGRD
+Adding data field: VGRD
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VVEL
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VVEL
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VVEL
+Adding data field: VVEL
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: HGT
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: HGT
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: HGT
+Adding data field: HGT
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::_shouldAddField
+  -->> adding field: Pressure
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: Pressure
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: Pressure
+Adding data field: Pressure
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::addDataFieldsTime elapsed = 0
+Adding chunk: NetCDF file global attributes
+Ncf2MdvTrans::addGlobalAttrXmlChunk()
+Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc
+       Station name : KDDC
+       Sounding time: 2021/07/06 23:00:00
+Mdvx::readVsection - reading file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+
+
+
SUCCESS - opened file: /tmp/lrose_data/nexrad_mosaic/mdv/ruc/20210706/20210706_230000.mdv.cf.nc
+SUCCESS - setting master header
+Default time dimension: time
+  time: 2021/07/06 23:00:00
+SUCCESS - setting time coord variable
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> rejecting field: mdv_chunk_0000
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: TMP
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: TMP
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: TMP
+Adding data field: TMP
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: RH
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: RH
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: RH
+Adding data field: RH
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: UGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: UGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: UGRD
+Adding data field: UGRD
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VGRD
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VGRD
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VGRD
+Adding data field: VGRD
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: VVEL
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: VVEL
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: VVEL
+Adding data field: VVEL
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: HGT
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: HGT
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: HGT
+Adding data field: HGT
+             time: 2021/07/06 23:00:00
+Ncf2MdvTrans::_shouldAddField
+  -->> adding field: Pressure
+Ncf2MdvTrans::_shouldAddField
+  Checking variable for field data: Pressure
+SUCCESS - FIELD has X coordinate
+SUCCESS - FIELD has Y coordinate
+NOTE - FIELD has Z coordinate
+Ncf2MdvTrans::_addOneField
+  -->> adding field: Pressure
+Adding data field: Pressure
+             time: 2021/07/06 23:00:00
+
+
+
Ncf2MdvTrans::addDataFieldsTime elapsed = 0
+Adding chunk: NetCDF file global attributes
+Ncf2MdvTrans::addGlobalAttrXmlChunk()
+Wrote spdb data, URL: /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc
+       Station name : KUEX
+       Sounding time: 2021/07/06 23:00:00
+
+
+
+
+
+
+

List sounding data base

+
+
+
# List SPDB files
+!ls -alR /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706*
+
+
+
+
+
-rw-r--r-- 1 root root 4704 Feb 21 08:12 /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706.data
+-rw-r--r-- 1 root root 6360 Feb 21 08:12 /tmp/lrose_data/nexrad_mosaic/spdb/sounding/ruc/20210706.indx
+
+
+
+
+
+
+

Run RadxRate on the CfRadial files

+

RadxRate will compute:

+
    +

  • KDP - specific differential phase

    • +

  • KDP_SC - KDP conditioned using Z and ZDR self-consistency

    • +

  • PID - NCAR Particle ID (type)

    • +

  • RATE_ZH - precip rate from standard ZR relationship

    • +

  • RATE_HYBRID - precip rate from NCAR hybrid estimator

    • +
+

RadxRate has a main parameter file, which then specifies a parameter file computing each of KDP, PID and precip rate.

+

The PID step requires an additional parameter file specifying the fuzzy logic thresholds.

+

The parameter files used here are:

+
    +

  • kdp_params.nexrad

    • +

  • pid_params.nexrad

    • +

  • pid_thresholds.nexrad

    • +

  • rate_params.nexrad

    • +
+
+
+
# Run RadxRate for 3 NEXRAD radars
+
+for radar_name in ['KGLD', 'KUEX', 'KDDC']:
+    # Set radar in name environment variable
+    os.environ['RADAR_NAME'] = radar_name
+    # Run RadxRate using param file
+    !/usr/local/lrose/bin/RadxRate -params ./params/RadxRate.nexrad -debug -start "2021 07 06 22 00 00" -end "2021 07 06 22 30 00"
+
+
+
+
+
RadxRate::_runArchive
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:00:03
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503182.558020.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503182.558020.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:04:48
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503191.864188.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503191.864188.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:09:35
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503200.847736.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503200.847736.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:14:20
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503209.774561.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503209.774561.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:19:06
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503218.729132.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503218.729132.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:23:50
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503227.849766.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503227.849766.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:28:34
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503236.838974.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/tmp.710.1708503236.838974.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+
+
+
RadxRate::_runArchive
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:02:49
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503247.322826.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503247.322826.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:07:23
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503256.404904.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503256.404904.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:12:04
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503265.424056.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503265.424056.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:16:33
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503274.456383.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503274.456383.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:21:02
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503283.376405.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503283.376405.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:25:31
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503292.330429.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/tmp.717.1708503292.330429.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+
+
+
RadxRate::_runArchive
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:00:00
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503302.412655.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503302.412655.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+
+
+
INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:04:30
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503311.439768.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503311.439768.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:09:21
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503320.227159.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503320.227159.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:16:00
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503329.222058.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503329.222058.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:20:51
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503338.205286.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503338.205286.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+INFO - RadxRate::Run
+  Input path: /tmp/lrose_data/nexrad_mosaic/cfradial/moments/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+
+
+
Thread #: 0
+  Loading temp profile for time: 2021/07/06 22:25:33
+
+
+
INFO: RadxFile::writeToDir
+  Writing CfRadial file to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToDir
+  Writing to dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+DEBUG - NcfRadxFile::writeToPath
+  Writing to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+  Tmp path is: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503347.236182.tmp
+  Writing fields and compressing ...
+
+
+
  ... writing field: RATE_ZH
+
+
+
  ... writing field: RATE_HYBRID
+
+
+
  ... writing field: PID
+
+
+
  ... writing field: KDP
+
+
+
  ... writing field: DBZ
+
+
+
DEBUG - NcfRadxFile::writeToPath
+  Renamed tmp path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/tmp.724.1708503347.236182.tmp
+     to final path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+INFO: RadxFile::writeToDir
+  Wrote CfRadial file to path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+
+
+
+
+
+
+

List files created by RadxRate

+
+
+
# List the CfRadial files created by RadxRate
+!ls -R ${NEXRAD_DATA_DIR}/cfradial/rate/K*/2*
+
+
+
+
+
/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706:
+cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+
+/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706:
+cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+
+/tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706:
+cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+
+
+
+
+
+
+

Plot PID and rate results for NEXRAD Goodland radar (KGLD)

+
+
+
# Read CfRadial file into radar object
+filePathRate = os.path.join(nexradDataDir, "cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc")
+rate_kgld = pyart.io.read_cfradial(filePathRate)
+rate_kgld.info('compact')
+
+
+
+
+
altitude: <ndarray of type: float64 and shape: (1,)>
+altitude_agl: <ndarray of type: float64 and shape: (1,)>
+antenna_transition: <ndarray of type: int8 and shape: (6120,)>
+azimuth: <ndarray of type: float32 and shape: (6120,)>
+elevation: <ndarray of type: float32 and shape: (6120,)>
+fields:
+	RATE_ZH: <ndarray of type: float32 and shape: (6120, 912)>
+	RATE_HYBRID: <ndarray of type: float32 and shape: (6120, 912)>
+	PID: <ndarray of type: float32 and shape: (6120, 912)>
+	KDP: <ndarray of type: float32 and shape: (6120, 912)>
+	DBZ: <ndarray of type: float32 and shape: (6120, 912)>
+fixed_angle: <ndarray of type: float32 and shape: (14,)>
+instrument_parameters:
+	follow_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	pulse_width: <ndarray of type: float32 and shape: (6120,)>
+	prt_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	prt: <ndarray of type: float32 and shape: (6120,)>
+	prt_ratio: <ndarray of type: float32 and shape: (6120,)>
+	polarization_mode: <ndarray of type: |S1 and shape: (14, 32)>
+	nyquist_velocity: <ndarray of type: float32 and shape: (6120,)>
+	unambiguous_range: <ndarray of type: float32 and shape: (6120,)>
+	n_samples: <ndarray of type: int32 and shape: (6120,)>
+	radar_antenna_gain_h: <ndarray of type: float32 and shape: (1,)>
+	radar_antenna_gain_v: <ndarray of type: float32 and shape: (1,)>
+	radar_beam_width_h: <ndarray of type: float32 and shape: (1,)>
+	radar_beam_width_v: <ndarray of type: float32 and shape: (1,)>
+	radar_rx_bandwidth: <ndarray of type: float32 and shape: (1,)>
+	measured_transmit_power_v: <ndarray of type: float32 and shape: (6120,)>
+	measured_transmit_power_h: <ndarray of type: float32 and shape: (6120,)>
+latitude: <ndarray of type: float64 and shape: (1,)>
+longitude: <ndarray of type: float64 and shape: (1,)>
+nsweeps: 14
+ngates: 912
+nrays: 6120
+radar_calibration:
+	r_calib_time: <ndarray of type: |S1 and shape: (1, 32)>
+	r_calib_pulse_width: <ndarray of type: float32 and shape: (1,)>
+	r_calib_xmit_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_xmit_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_waveguide_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_waveguide_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_radome_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_two_way_radome_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_mismatch_loss: <ndarray of type: float32 and shape: (1,)>
+	r_calib_k_squared_water: <ndarray of type: float32 and shape: (1,)>
+	r_calib_radar_constant_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_radar_constant_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_antenna_gain_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_antenna_gain_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_i0_dbm_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_gain_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_receiver_slope_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dynamic_range_db_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_base_dbz_1km_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_hc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_vc: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_hx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_sun_power_vx: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_source_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_noise_source_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_power_measure_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_power_measure_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_coupler_forward_loss_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_coupler_forward_loss_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_dbz_correction: <ndarray of type: float32 and shape: (1,)>
+	r_calib_zdr_correction: <ndarray of type: float32 and shape: (1,)>
+	r_calib_ldr_correction_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_ldr_correction_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_system_phidp: <ndarray of type: float32 and shape: (1,)>
+	r_calib_test_power_h: <ndarray of type: float32 and shape: (1,)>
+	r_calib_test_power_v: <ndarray of type: float32 and shape: (1,)>
+	r_calib_index: <ndarray of type: int32 and shape: (6120,)>
+range: <ndarray of type: float32 and shape: (912,)>
+scan_rate: <ndarray of type: float32 and shape: (6120,)>
+scan_type: other
+sweep_end_ray_index: <ndarray of type: int32 and shape: (14,)>
+sweep_mode: <ndarray of type: |S1 and shape: (14, 32)>
+sweep_number: <ndarray of type: int32 and shape: (14,)>
+sweep_start_ray_index: <ndarray of type: int32 and shape: (14,)>
+target_scan_rate: <ndarray of type: float32 and shape: (14,)>
+time: <ndarray of type: float64 and shape: (6120,)>
+metadata:
+	Conventions: CF-1.7
+	Sub_conventions: CF-Radial instrument_parameters radar_parameters radar_calibration
+	version: CF-Radial-1.4
+	title: 
+	institution: 
+	references: 
+	source: ARCHIVE 2 data
+	history: 
+	comment: 
+	original_format: NEXRAD
+	driver: RadxConvert(NCAR)
+	created: 2024/02/21 08:11:20.711
+	start_datetime: 2021-07-06T22:00:03Z
+	time_coverage_start: 2021-07-06T22:00:03Z
+	start_time: 2021-07-06 22:00:03.963
+	end_datetime: 2021-07-06T22:04:39Z
+	time_coverage_end: 2021-07-06T22:04:39Z
+	end_time: 2021-07-06 22:04:39.770
+	instrument_name: KGLD
+	site_name: DLGK
+	scan_name: Surveillance
+	scan_id: 212
+	platform_is_mobile: false
+	n_gates_vary: false
+	ray_times_increase: true
+	volume_number: 79
+	platform_type: fixed
+	instrument_type: radar
+	primary_axis: axis_z
+
+
+
+
+
+
+
# Plot results of RadxRate
+
+displayRate = pyart.graph.RadarDisplay(rate_kgld)
+figRate = plt.figure(1, (12, 10))
+
+# DBZ (input)
+
+axDbz = figRate.add_subplot(221)
+displayRate.plot_ppi('DBZ', 0, vmin=-32, vmax=64.,
+                    axislabels=("x(km)", "y(km)"),
+                    colorbar_label="DBZ")
+displayRate.plot_range_rings([50, 100, 150, 200])
+displayRate.plot_cross_hair(200.)
+
+# KDP (computed)
+
+axKdp = figRate.add_subplot(222)
+displayRate.plot_ppi('KDP', 0, vmin=0, vmax=2.,
+    axislabels=("x(km)", "y(km)"),
+    colorbar_label="KDP (deg/km)",
+    cmap="rainbow")
+displayRate.plot_range_rings([50, 100, 150, 200])
+displayRate.plot_cross_hair(200.)
+
+# RATE_HYBRID (computed)
+
+axHybrid = figRate.add_subplot(223)
+displayRate.plot_ppi('RATE_HYBRID', 0, vmin=0, vmax=50.,
+    axislabels=("x(km)", "y(km)"),
+    colorbar_label="RATE_HYBRID(mm/hr)",
+    cmap = "rainbow")
+displayRate.plot_range_rings([50, 100, 150, 200])
+displayRate.plot_cross_hair(200.)
+
+# NCAR PID (computed)
+
+axPID = figRate.add_subplot(224)
+displayRate.plot_ppi('PID', 0,
+    axislabels=("x(km)", "y(km)"),
+    colorbar_label="PID",
+    cmap = "rainbow")
+displayRate.plot_range_rings([50, 100, 150, 200])
+displayRate.plot_cross_hair(200.)
+
+pid_cbar = displayRate.cbs[3]
+pid_cbar.set_ticks([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17])
+pid_cbar.set_ticklabels(['cld-drops', 'drizzle', 'lt-rain', 'mod-rain', 'hvy-rain', 'hail', 'rain/hail', 'sm-hail', 'gr/rain', 'dry-snow', 'wet-snow', 'ice', 'irreg-ice', 'slw', 'insects', '2nd-trip', 'clutter'])
+
+figRate.tight_layout()
+
+plt.show()
+
+
+
+
+../../_images/nexrad_mosaic.erad_tutorial_30_0.png +
+
+
+
+

Convert CfRadial polar files to Cartesian

+
+
+
# Run Radx2Grid for 3 NEXRAD radars
+
+for radar_name in ['KGLD', 'KUEX', 'KDDC']:
+    # Set radar in name environment variable
+    os.environ['RADAR_NAME'] = radar_name
+    # Run RadxRate using param file
+    !/usr/local/lrose/bin/Radx2Grid -params ./params/Radx2Grid.rate -debug -start "2021 07 06 22 00 00" -end "2021 07 06 22 30 00"
+
+
+
+
+
======================================================================
+Program 'Radx2Grid'
+Run-time 2024/02/21 08:15:51.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+Running Radx2Grid in ARCHIVE mode
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220003.963_to_20210706_220439.770_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.359917
+TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.00012
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.158769
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000242
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.1e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.4e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.280472
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.3e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63356
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 6.2e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.3761
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 0.000103
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220003.nc
+TIMING, task: Writing output files, secs used: 1.41969
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220448.793_to_20210706_220926.383_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.57039
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.176296
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.14962
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000273
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.0001
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.8e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.29795
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.6e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.62658
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 4.5e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.37881
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.7e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220448.nc
+TIMING, task: Writing output files, secs used: 1.37182
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_220935.631_to_20210706_221411.627_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.570139
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164678
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.117063
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000251
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.1e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.3e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.216106
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 8.4e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.66949
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 7.3e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.36629
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.8e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+
+
+
Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220935.nc
+TIMING, task: Writing output files, secs used: 1.36262
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221420.555_to_20210706_221857.324_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.555814
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164377
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111029
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000251
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.5e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.2e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.169891
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.2e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63026
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 6.4e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.37549
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.3e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc
+TIMING, task: Writing output files, secs used: 1.31318
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_221906.199_to_20210706_222341.850_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.565229
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164503
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111093
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000266
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000109
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.5e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.201059
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.3e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63767
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.35535
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+
+
+
Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221906.nc
+TIMING, task: Writing output files, secs used: 1.34256
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222350.154_to_20210706_222826.584_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.558327
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164603
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111361
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.00027
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.8e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.9e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.176899
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.3e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.6353
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.9e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.36724
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.4e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222350.nc
+TIMING, task: Writing output files, secs used: 1.31177
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KGLD/20210706/cfrad.20210706_222834.963_to_20210706_223310.845_KGLD_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.569528
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.163839
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111033
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000257
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.00012
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.8e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.186865
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.2e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.6326
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.5e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.34536
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.4e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222834.nc
+TIMING, task: Writing output files, secs used: 1.38191
+
+
+
======================================================================
+Program 'Radx2Grid'
+Run-time 2024/02/21 08:16:29.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+Running Radx2Grid in ARCHIVE mode
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220249.032_to_20210706_220715.866_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.347416
+TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.000133
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.124374
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000271
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000108
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.5e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.216987
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 7.1e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.6486
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 6.9e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.42908
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.8e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+
+
+
Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc
+TIMING, task: Writing output files, secs used: 1.36329
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_220723.969_to_20210706_221157.362_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.557427
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.176159
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.123856
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.00023
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 8e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.3e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.195838
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.62214
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 4.9e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.41979
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.7e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220723.nc
+TIMING, task: Writing output files, secs used: 1.34921
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221204.520_to_20210706_221625.502_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.562014
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.163878
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111664
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000237
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.3e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.2e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.190328
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.3e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63145
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 4.8e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.42223
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.3e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221204.nc
+TIMING, task: Writing output files, secs used: 1.31587
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_221633.850_to_20210706_222054.868_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.551677
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.163998
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111935
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000281
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000132
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 3.6e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.172176
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.62548
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 4.3e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.44047
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.8e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc
+TIMING, task: Writing output files, secs used: 1.28684
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222102.216_to_20210706_222523.504_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.553706
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.163863
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.112815
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.00027
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000123
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.6e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.196431
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.2e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63035
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.7e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.41803
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 6.6e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222102.nc
+TIMING, task: Writing output files, secs used: 1.31647
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KUEX/20210706/cfrad.20210706_222531.244_to_20210706_222952.818_KUEX_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.552593
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164275
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.112151
+  _scanDeltaAz: 1
+  _scanDeltaEl: 3.91113
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000229
+  _searchRadiusEl: 5.80113
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 9.4e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.9e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.174155
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.7e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63016
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 7.9e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.42487
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc
+TIMING, task: Writing output files, secs used: 1.30117
+
+
+
======================================================================
+Program 'Radx2Grid'
+Run-time 2024/02/21 08:17:03.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+Running Radx2Grid in ARCHIVE mode
+  Input dir: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC
+  Start time: 2021/07/06 22:00:00
+  End time: 2021/07/06 22:30:00
+INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220000.765_to_20210706_220422.888_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.343288
+TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.00014
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.123304
+  _scanDeltaAz: 0.6
+  _scanDeltaEl: 2.02148
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000271
+  _searchRadiusEl: 3.91148
+  _searchRadiusAz: 2.49
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 25
+TIMING, task: Computing search limits, secs used: 0.000108
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2.2e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.112095
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 6.1e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63582
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.2e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.3407
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.9e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+
+
+
Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220000.nc
+TIMING, task: Writing output files, secs used: 1.33703
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220430.757_to_20210706_220912.758_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.559426
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.181967
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.123219
+  _scanDeltaAz: 1
+  _scanDeltaEl: 2.46094
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000257
+  _searchRadiusEl: 4.35094
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.00013
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 2e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.133709
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 7.6e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.64269
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.36868
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.6e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220430.nc
+TIMING, task: Writing output files, secs used: 1.29872
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_220921.610_to_20210706_221350.957_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.549631
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164552
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.112392
+  _scanDeltaAz: 0.6
+  _scanDeltaEl: 2.63672
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000248
+  _searchRadiusEl: 4.52672
+  _searchRadiusAz: 2.49
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 25
+TIMING, task: Computing search limits, secs used: 0.00011
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.9e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.101315
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.6e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63478
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.2e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.34539
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.7e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+
+
+
Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220921.nc
+TIMING, task: Writing output files, secs used: 1.2786
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_221600.999_to_20210706_222043.450_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.558307
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164934
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.112546
+  _scanDeltaAz: 1
+  _scanDeltaEl: 2.46094
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000261
+  _searchRadiusEl: 4.35094
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 7.9e-05
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.3e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.115266
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 4.9e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.63929
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.3e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.37254
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc
+TIMING, task: Writing output files, secs used: 1.28427
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222051.218_to_20210706_222526.565_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.562909
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164437
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.11172
+  _scanDeltaAz: 1
+  _scanDeltaEl: 2.63672
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000269
+  _searchRadiusEl: 4.52672
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000109
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.6e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.119245
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.2e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.64182
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 5.5e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.37273
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 4.8e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222051.nc
+TIMING, task: Writing output files, secs used: 1.28411
+
+
+
INFO - Radx2Grid::_processFile
+  Input file path: /tmp/lrose_data/nexrad_mosaic/cfradial/rate/KDDC/20210706/cfrad.20210706_222533.934_to_20210706_223025.212_KDDC_SUR.nc
+  Reading in file ...
+
+
+
TIMING, task: Cart interp - reading data, secs used: 0.559456
+
+
+
TIMING, task: Cart interp - before _initOutputArrays, secs used: 0.164609
+
+
+
TIMING, task: Cart interp - after _initOutputArrays, secs used: 0.111941
+  _scanDeltaAz: 1
+  _scanDeltaEl: 2.46094
+  _isSector: 0
+  _spansNorth: N
+TIMING, task: Cart interp - before computeSearchLimits, secs used: 0.000223
+  _searchRadiusEl: 4.35094
+  _searchRadiusAz: 2.89
+  _searchMinAz: 0
+  _searchNAz: 3801
+  _searchMaxDistAz: 29
+TIMING, task: Computing search limits, secs used: 0.000101
+  Filling search matrix ... 
+TIMING, task: Cart interp - before fillSearchMatrix, secs used: 1.6e-05
+
+
+
TIMING, task: Filling search matrix, secs used: 0.114769
+  Computing grid relative to radar ... 
+TIMING, task: Cart interp - before _computeGridRelative, secs used: 5.6e-05
+
+
+
TIMING, task: Computing grid relative to radar, secs used: 1.64066
+  Interpolating ... 
+TIMING, task: Cart interp - before doInterp, secs used: 6.1e-05
+
+
+
TIMING, task: Interpolating, secs used: 1.36246
+TIMING, task: Cart interp - before _writeOutputFile, secs used: 5.5e-05
+  Writing output file ... 
+  Adding field: RATE_HYBRID
+
+
+
  Adding field: PID
+
+
+
  Adding field: DBZ
+
+
+
  Adding field: range
+
+
+
  Adding field: Coverage
+
+
+
Mdv2NcfTrans::addGlobalAttributes()
+Mdv2NcfTrans::addDimensions()
+Mdv2NetCDF::_addTimeVariables()
+Mdv2NcfTrans::addCoordinateVariables()
+Mdv2NcfTrans::addFieldVariables()
+
+
+
adding field: RATE_HYBRID
+NcfFieldData::_setChunking()
+  Field: RATE_HYBRID
+  nyChunk: 460
+  nxChunk: 460
+adding field: PID
+NcfFieldData::_setChunking()
+  Field: PID
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: DBZ
+NcfFieldData::_setChunking()
+  Field: DBZ
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: range
+NcfFieldData::_setChunking()
+  Field: range
+  nyChunk: 460
+  nxChunk: 460
+
+
+
adding field: Coverage
+NcfFieldData::_setChunking()
+  Field: Coverage
+  nyChunk: 460
+  nxChunk: 460
+Mdv2NcfTrans::_putTimeVariables()
+Mdv2NcfTrans::_putCoordinateVariables()
+Mdv2NcfTrans::_putFieldDataVariables()
+
+
+
OutputMdv::_writeLdataInfo(): Data written to /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc
+TIMING, task: Writing output files, secs used: 1.29409
+
+
+
+
+
+
+

List files created by Radx2Grid

+
+
+
# List the Cartesian files created by Radx2Grid
+!ls -R ${NEXRAD_DATA_DIR}/mdv/radarCart/K*/2*
+
+
+
+
+
/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706:
+ncf_20210706_220000.nc	ncf_20210706_220921.nc	ncf_20210706_222051.nc
+ncf_20210706_220430.nc	ncf_20210706_221600.nc	ncf_20210706_222533.nc
+
+/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706:
+ncf_20210706_220003.nc	ncf_20210706_221420.nc	ncf_20210706_222834.nc
+ncf_20210706_220448.nc	ncf_20210706_221906.nc
+ncf_20210706_220935.nc	ncf_20210706_222350.nc
+
+/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706:
+ncf_20210706_220249.nc	ncf_20210706_221204.nc	ncf_20210706_222102.nc
+ncf_20210706_220723.nc	ncf_20210706_221633.nc	ncf_20210706_222531.nc
+
+
+
+
+
+
+

Merge Cartesian files into mosaic, using MdvMerge2

+
+
+
# Run MdvMerge2 to merrge the Cart data from 3 radars into a mosaic
+!/usr/local/lrose/bin/MdvMerge2 -params params/MdvMerge2.mosaic -start "2021 07 06 22 00 00" -end "2021 07 06 22 30 00" -debug
+
+
+
+
+
======================================================================
+Program 'MdvMerge2'
+Run-time 2024/02/21 08:17:35.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+Archive trigger
+  Start time: 2021/07/06 22:00:00
+  End   time: 2021/07/06 22:30:00
+  Prev  time: 2021/07/06 21:55:00
+  Next  time: 2021/07/06 22:00:00
+Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Field names: DBZ RATE_HYBRID PID range
+Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Field names: DBZ RATE_HYBRID PID range
+Creating InputFile for url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Field names: DBZ RATE_HYBRID PID range
+  Next trigger: 2021/07/06 22:00:00
+----> Trigger time: 2021/07/06 22:00:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:00:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220003.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:00:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:00:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220000.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:00:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220000.mdv.cf.nc
+  Next trigger: 2021/07/06 22:05:00
+----> Trigger time: 2021/07/06 22:05:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:05:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220448.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:05:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_220249.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:05:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220430.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:05:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220500.mdv.cf.nc
+  Next trigger: 2021/07/06 22:10:00
+----> Trigger time: 2021/07/06 22:10:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:10:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_220935.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:10:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221204.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:10:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_220921.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:10:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221000.mdv.cf.nc
+  Next trigger: 2021/07/06 22:15:00
+----> Trigger time: 2021/07/06 22:15:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:15:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:15:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:15:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:15:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc
+  Next trigger: 2021/07/06 22:20:00
+----> Trigger time: 2021/07/06 22:20:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:20:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221906.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:20:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222102.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:20:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222051.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:20:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222000.mdv.cf.nc
+  Next trigger: 2021/07/06 22:25:00
+----> Trigger time: 2021/07/06 22:25:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:25:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222350.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:25:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:25:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:25:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222500.mdv.cf.nc
+  Next trigger: 2021/07/06 22:30:00
+----> Trigger time: 2021/07/06 22:30:00
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:30:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_222834.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:30:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_222531.nc
+
+
+
READ_CLOSEST
+Reading data for URL: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+Mdvx read request
+-----------------
+  Encoding type: ENCODING_FLOAT32 (FLOAT)
+  Compression type: COMPRESSION_NONE
+  Scaling type: SCALING_ROUNDED
+  Composite?: 0
+  FillMissing?: 0
+  Field names: DBZ, RATE_HYBRID, PID, range
+  FieldFileHeaders?: 0
+  Search mode: READ_CLOSEST
+  Search time: 2021/07/06 22:30:00
+  Search margin: 600 secs
+  Read dir: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+  Read32BitHeaders?: false
+  Read dir url: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC
+
+
+
  Read data from file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_222533.nc
+
+
+
Writing merged MDV file, time 2021/07/06 22:30:00 to URL /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_223000.mdv.cf.nc
+  Next trigger: 2021/07/06 22:35:00
+  Done
+
+
+
+
+
+
+
# List the mosaic files created by MdvMerge2
+!ls -R ${NEXRAD_DATA_DIR}/mdv/radarCart/mosaic/2*
+
+
+
+
+
/tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706:
+20210706_220000.mdv.cf.nc  20210706_221500.mdv.cf.nc  20210706_223000.mdv.cf.nc
+20210706_220500.mdv.cf.nc  20210706_222000.mdv.cf.nc
+20210706_221000.mdv.cf.nc  20210706_222500.mdv.cf.nc
+
+
+
+
+
+
+

Read in file from radar mosaic

+
+
+
# Read in example radar mosaic for a single time
+
+filePathMosaic = os.path.join(nexradDataDir, 'mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc')
+dsMosaic = nc.Dataset(filePathMosaic)
+print("Radar mosaic file path: ", filePathMosaic)
+print("Radar mosaic data set: ", dsMosaic)
+
+# Compute time
+
+uTimeSecs = dsMosaic['start_time'][0]
+startTime = datetime.datetime.fromtimestamp(int(uTimeSecs))
+startTimeStr = startTime.strftime('%Y/%m/%d-%H:%M:%S UTC')
+print("Start time: ", startTimeStr)
+
+# print(dsMosaic['DBZ'])
+#for dim in dsMosaic.dimensions.values():
+#    print(dim)
+#for var in dsMosaic.variables.values():
+#    print("========================================")
+#    print(var)
+#    print("========================================")
+
+# create 3D dbz array with nans for missing vals
+
+dsDbz = dsMosaic['DBZ']
+dbz3D = np.array(dsDbz)
+fillValue = dsDbz._FillValue
+# print("fillValue: ", fillValue)
+
+# if 4D (i.e. time is dim0) change to 3D
+
+if (len(dbz3D.shape) == 4):
+    dbz3D = dbz3D[0]
+
+# Compute mosaic grid limits
+
+(nZMosaic, nYMosaic, nXMosaic) = dbz3D.shape
+lon = np.array(dsMosaic['x0'])
+lat = np.array(dsMosaic['y0'])
+ht = np.array(dsMosaic['z0'])
+dLonMosaic = lon[1] - lon[0]
+dLatMosaic = lat[1] - lat[0]
+minLonMosaic = lon[0] - dLonMosaic / 2.0
+maxLonMosaic = lon[-1] + dLonMosaic / 2.0
+minLatMosaic = lat[0] - dLatMosaic / 2.0
+maxLatMosaic = lat[-1] + dLatMosaic / 2.0
+minHtMosaic = ht[0]
+maxHtMosaic = ht[-1]
+
+print("minLonMosaic, maxLonMosaic: ", minLonMosaic, maxLonMosaic)
+print("minLatMosaic, maxLatMosaic: ", minLatMosaic, maxLatMosaic)
+print("minHt, maxHt: ", minHtMosaic, maxHtMosaic)
+print("hts: ", ht)
+del lon, lat, ht
+
+# Compute column-max reflectivity
+
+dbzPlaneMax = np.amax(dbz3D, (0))
+dbzPlaneMax[dbzPlaneMax == fillValue] = np.nan
+
+print("Shape of composite DBZ grid: ", dbzPlaneMax.shape)
+#print(dbzPlaneMax[dbzPlaneMax != np.nan])
+#print(np.min(dbzPlaneMax[dbzPlaneMax != np.nan]))
+#print(np.max(dbzPlaneMax))
+
+
+
+
+
Radar mosaic file path:  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc
+Radar mosaic data set:  <class 'netCDF4._netCDF4.Dataset'>
+root group (NETCDF4 data model, file format HDF5):
+    Conventions: CF-1.6
+    history: Data merged from following files:
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc
+
+    source: NEXRAD radars
+    title: 3D RADAR MOSAIC
+    comment: 
+    dimensions(sizes): time(1), bounds(2), x0(900), y0(700), z0(31)
+    variables(dimensions): float64 time(time), float64 start_time(time), float64 stop_time(time), float64 time_bounds(time, bounds), float32 x0(x0), float32 y0(y0), float32 z0(z0), int32 grid_mapping_0(), int32 mdv_master_header(time), int16 DBZ(time, z0, y0, x0), int16 RATE_HYBRID(time, z0, y0, x0), int16 PID(time, z0, y0, x0), int16 range(time, z0, y0, x0)
+    groups: 
+Start time:  2021/07/06-22:14:20 UTC
+
+
+
minLonMosaic, maxLonMosaic:  -104.50500106811523 -95.50500106811523
+minLatMosaic, maxLatMosaic:  35.4950008392334 42.49499702453613
+minHt, maxHt:  1.25 20.0
+hts:  [ 1.25  1.5   1.75  2.    2.25  2.5   2.75  3.    3.5   4.    4.5   5.
+  5.5   6.    6.5   7.    7.5   8.    8.5   9.   10.   11.   12.   13.
+ 14.   15.   16.   17.   18.   19.   20.  ]
+Shape of composite DBZ grid:  (700, 900)
+
+
+
+
+
+

Create map for Cartesian grid plotting

+
+
+
# Create map for plotting lat/lon grids
+def new_map(fig):
+    
+    ## Create projection centered on data
+    proj = ccrs.PlateCarree()
+
+    ## New axes with the specified projection:
+    ax = fig.add_subplot(1, 1, 1, projection=proj)
+    
+    ## Set extent the same as radar mosaic
+    ax.set_extent([minLonMosaic, maxLonMosaic, minLatMosaic, maxLatMosaic])
+
+    ## Add grid lines & labels:
+    gl = ax.gridlines( crs=ccrs.PlateCarree()
+                     , draw_labels=True
+                     , linewidth=1
+                     , color='lightgray'
+                     , alpha=0.5, linestyle='--'
+                     ) 
+    gl.top_labels = False
+    gl.left_labels = True
+    gl.right_labels = False
+    gl.xlines = True
+    gl.ylines = True
+    gl.xformatter = LONGITUDE_FORMATTER
+    gl.yformatter = LATITUDE_FORMATTER
+    gl.xlabel_style = {'size': 8, 'weight': 'bold'}
+    gl.ylabel_style = {'size': 8, 'weight': 'bold'}
+    
+    return ax
+
+
+
+
+
+
+

Plot column-max reflectivity in plan view, and a N/S and W/E cross section of reflectivity

+
+
+
# Plot column-max reflectivity
+figDbzComp = plt.figure(figsize=(8, 8), dpi=150)
+axDbzComp = new_map(figDbzComp)
+plt.imshow(dbzPlaneMax,
+            cmap='pyart_Carbone42',
+            interpolation = 'bilinear',
+            origin = 'lower',
+            extent = (minLonMosaic, maxLonMosaic, minLatMosaic, maxLatMosaic))
+axDbzComp.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='black')
+axDbzComp.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')
+#axDbzComp.coastlines('10m', 'darkgray', linewidth=1, zorder=0)
+plt.colorbar(label="DBZ", orientation="vertical", shrink=0.5)
+plt.title("Radar mosaic column-max DBZ: " + startTimeStr)
+
+
+
+
+
Text(0.5, 1.0, 'Radar mosaic column-max DBZ: 2021/07/06-22:14:20 UTC')
+
+
+../../_images/nexrad_mosaic.erad_tutorial_43_1.png +
+
+
+
+
# Compute W-E DBZ vertical section
+nYHalfMosaic = int(nYMosaic/2)
+dbzVertWE = dbz3D[:, nYHalfMosaic:(nYHalfMosaic+1), :]
+dbzVertWE = dbzVertWE.reshape(dbzVertWE.shape[0], dbzVertWE.shape[2])
+print('dbzVertWE.shape: ', dbzVertWE.shape)
+dbzVertWE[dbzVertWE == fillValue] = np.nan
+dbzVertWEMax = np.amax(dbz3D, axis=1)
+dbzVertWEMax[dbzVertWEMax == fillValue] = np.nan
+print('dbzVertWEMax.shape: ', dbzVertWEMax.shape)
+
+# Compute DBZ N-S vertical section
+nXHalfMosaic = int(nXMosaic/2)
+dbzVertNS = dbz3D[:, :, nXHalfMosaic:(nXHalfMosaic+1)]
+dbzVertNS = dbzVertNS.reshape(dbzVertNS.shape[0], dbzVertNS.shape[1])
+dbzVertNS[dbzVertNS == fillValue] = np.nan
+print('dbzVertNS.shape: ', dbzVertNS.shape)
+dbzVertNSMax = np.amax(dbz3D, axis=2)
+dbzVertNSMax[dbzVertNSMax == fillValue] = np.nan
+print('dbzVertNSMax.shape: ', dbzVertNSMax.shape)
+
+
+
+
+
dbzVertWE.shape:  (31, 900)
+dbzVertWEMax.shape:  (31, 900)
+dbzVertNS.shape:  (31, 700)
+dbzVertNSMax.shape:  (31, 700)
+
+
+
+
+
+
+
# Plot mid W-E DBZ vertical section
+
+fig2 = plt.figure(num=2, figsize=[12, 8], layout='constrained')
+ax2a = fig2.add_subplot(211, xlim = (minLonMosaic, maxLonMosaic), ylim = (minHtMosaic, maxHtMosaic))
+plt.imshow(dbzVertWE,
+    cmap='pyart_Carbone42',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLonMosaic, maxLonMosaic, minHtMosaic, maxHtMosaic))
+ax2a.set_aspect(0.15)
+ax2a.set_xlabel('Longitude (deg)')
+ax2a.set_ylabel('Height (km)')
+plt.title("Vert slice mid W-E DBZ: " + startTimeStr)
+
+# Plot mid N-S DBZ vertical section
+
+ax2b = fig2.add_subplot(212, xlim = (minLatMosaic, maxLatMosaic), ylim = (minHtMosaic, maxHtMosaic))
+plt.imshow(dbzVertNS,
+    cmap='pyart_Carbone42',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLatMosaic, maxLatMosaic, minHtMosaic, maxHtMosaic))
+ax2b.set_aspect(0.15)
+ax2b.set_xlabel('Latitude (deg)')
+ax2b.set_ylabel('Height (km)')
+plt.title("Vert slice mid NS DBZ: " + startTimeStr)
+
+plt.colorbar(label="DBZ", orientation="horizontal", fraction=0.1)
+
+
+
+
+
<matplotlib.colorbar.Colorbar at 0x7f251dba29d0>
+
+
+../../_images/nexrad_mosaic.erad_tutorial_45_1.png +
+
+
+
+

View the Ecco parameter file

+

The paths of the input files, and the output results file, are specified in the parameters.

+

Also included are all of the parameters used to control the algorithm.

+
+
+
# View the param file
+!cat ./params/Ecco.nexrad_mosaic
+
+
+
+
+
/**********************************************************************
+ * TDRP params for Ecco
+ **********************************************************************/
+
+//======================================================================
+//
+// Program name: Ecco.
+//
+// Ecco finds convective and stratiform regions within a Cartesian radar 
+//   volume.
+//
+//======================================================================
+ 
+//======================================================================
+//
+// PROCESS CONTROL.
+//
+//======================================================================
+ 
+///////////// debug ///////////////////////////////////
+//
+// Debug option.
+//
+// If set, debug messages will be printed appropriately.
+//
+//
+// Type: enum
+// Options:
+//     DEBUG_OFF
+//     DEBUG_NORM
+//     DEBUG_VERBOSE
+//     DEBUG_EXTRA
+//
+
+debug = DEBUG_OFF;
+
+///////////// instance ////////////////////////////////
+//
+// Process instance.
+//
+// Used for registration with procmap.
+//
+//
+// Type: string
+//
+
+instance = "nexrad_mosaic";
+
+///////////// mode ////////////////////////////////////
+//
+// Operating mode.
+//
+// In REALTIME mode, the program waits for a new input file. In ARCHIVE 
+//   mode, it moves through the data between the start and end times set 
+//   on the command line. In FILELIST mode, it moves through the list of 
+//   file names specified on the command line.
+//
+//
+// Type: enum
+// Options:
+//     ARCHIVE
+//     REALTIME
+//     FILELIST
+//
+
+mode = ARCHIVE;
+
+///////////// use_multiple_threads ////////////////////
+//
+// Option to use multiple threads for speed.
+//
+// Computing the texture is the most time consuming step. If this is 
+//   true, then the texture will be computer for each vertical level in a 
+//   separate thread, in parallel. This speeds up the processing. If this 
+//   is false, the threads will be called serially. This is useful for 
+//   debugging.
+//
+//
+// Type: boolean
+//
+
+use_multiple_threads = TRUE;
+
+//======================================================================
+//
+// DATA INPUT.
+//
+//======================================================================
+ 
+///////////// input_url ///////////////////////////////
+//
+// URL for input data.
+//
+// This is used in REALTIME and ARCHIVE modes only. In FILELIST mode, 
+//   the file paths are specified on the command line.
+//
+//
+// Type: string
+//
+
+input_url = "$(NEXRAD_DATA_DIR)/mdv/radarCart/mosaic";
+
+///////////// dbz_field_name //////////////////////////
+//
+// dBZ field name in input MDV files.
+//
+//
+// Type: string
+//
+
+dbz_field_name = "DBZ";
+
+//======================================================================
+//
+// ALGORITHM PARAMETERS.
+//
+//======================================================================
+ 
+///////////// min_valid_height ////////////////////////
+//
+// Min height used in analysis (km).
+//
+// Only data at or above this altitude is used.
+//
+//
+// Type: double
+//
+
+min_valid_height = 0;
+
+///////////// max_valid_height ////////////////////////
+//
+// Max height used in analysis (km).
+//
+// Only data at or below this altitude is used.
+//
+//
+// Type: double
+//
+
+max_valid_height = 25;
+
+///////////// min_valid_dbz ///////////////////////////
+//
+// Minimum reflectivity threshold for this analysis (dBZ).
+//
+// Reflectivity below this threshold is set to missing.
+//
+//
+// Type: double
+//
+
+min_valid_dbz = 0;
+
+///////////// base_dbz ////////////////////////////////
+//
+// Set base DBZ value.
+//
+// Before computing the texture, we subtract the baseDBZ from the 
+//   measured DBZ. This adjusts the DBZ values into the positive range. 
+//   For S-, C- and X-band radars, this can be set to 0 dBZ, which is the 
+//   default. For Ka-band radars this should be around -10 dBZ. For W-band 
+//   radars -20 dBZ is appropriate.
+//
+//
+// Type: double
+//
+
+base_dbz = 0;
+
+///////////// min_valid_volume_for_convective /////////
+//
+// Min volume of a convective region (km3).
+//
+// Regions of smaller volume will be labeled SMALL.
+//
+//
+// Type: double
+//
+
+min_valid_volume_for_convective = 20;
+
+///////////// min_vert_extent_for_convective //////////
+//
+// Min vertical echo extent of a convective region (km).
+//
+// The vertical extent is computed as the mid height of the top layer in 
+//   the echo minus the mid height of the bottom layer. For an echo that 
+//   exists in only one layer, the vertical extent would therefore be 
+//   zero. This parameter lets us require that a valid convective echo 
+//   exist in multiple layers, which is desirable and helps to remove 
+//   spurious echoes as candidates for convection.
+//
+//
+// Type: double
+//
+
+min_vert_extent_for_convective = 3;
+
+///////////// dbz_for_echo_tops ///////////////////////
+//
+// Reflectivity for determing echo tops.
+//
+// Echo tops are defined as the max ht with reflectivity at or above 
+//   this value.
+//
+//
+// Type: double
+//
+
+dbz_for_echo_tops = 18;
+
+//======================================================================
+//
+// COMPUTING REFLECTIVITY TEXTURE.
+//
+//======================================================================
+ 
+///////////// texture_radius_km ///////////////////////
+//
+// Radius for texture analysis (km).
+//
+// We determine the reflectivity 'texture' at a point by computing the 
+//   standard deviation of the square of the reflectivity, for all grid 
+//   points within this radius of the central point. We then compute the 
+//   square root of that sdev.
+//
+//
+// Type: double
+//
+
+texture_radius_km = 7;
+
+///////////// min_valid_fraction_for_texture //////////
+//
+// Minimum fraction of surrounding points for texture computations.
+//
+// For a valid computation of texture, we require at least this fraction 
+//   of points around the central point to have valid reflectivity.
+//
+//
+// Type: double
+//
+
+min_valid_fraction_for_texture = 0.25;
+
+///////////// min_valid_fraction_for_fit //////////////
+//
+// Minimum fraction of surrounding points for 2D fit to DBZ.
+//
+// We compute a 2D fit to the reflectivity around a grid point, to 
+//   remove any systematic gradient. For a valid fit, we require at least 
+//   this fraction of points around the central point to have valid 
+//   reflectivity.
+//
+//
+// Type: double
+//
+
+min_valid_fraction_for_fit = 0.67;
+
+//======================================================================
+//
+// CONVERTING REFLECTIVITY TEXTURE TO CONVECTIVITY.
+//
+// Convectivity ranges from 0 to 1. To convert texture to convectivity, 
+//   we apply a piece-wise linear transfer function. This section defines 
+//   the lower texture limit and the upper texture limit. At or below the 
+//   lower limit convectivity is set to 0. At or above the upper limit 
+//   convectivity is set to 1. Between these two limits convectivity 
+//   varies linearly with texture.
+//
+//======================================================================
+ 
+///////////// texture_limit_low ///////////////////////
+//
+// Lower limit for texture.
+//
+// Below this texture the convectivity is set to 0.
+//
+//
+// Type: double
+//
+
+texture_limit_low = 0;
+
+///////////// texture_limit_high //////////////////////
+//
+// Upper limit for texture.
+//
+// Above this texture the convectivity is set to 1. Between the limits 
+//   convectivity varies linearly with texture.
+//
+//
+// Type: double
+//
+
+texture_limit_high = 30;
+
+//======================================================================
+//
+// SETTING CONVECTIVE OR STRATIFORM FLAGS BASED ON CONVECTIVITY.
+//
+// If neither is set, we flag the point as MIXED.
+//
+//======================================================================
+ 
+///////////// min_convectivity_for_convective /////////
+//
+// Minimum convectivity for convective at a point.
+//
+// If the convectivity at a point exceeds this value, we set the 
+//   convective flag at this point.
+//
+//
+// Type: double
+//
+
+min_convectivity_for_convective = 0.5;
+
+///////////// max_convectivity_for_stratiform /////////
+//
+// Maximum convectivity for stratiform at a point.
+//
+// If the convectivity at a point is less than this value, we set the 
+//   stratiform flag at this point. If it is above this but less than 
+//   min_convectivity_for_convective we flag the point as MIXED.
+//
+//
+// Type: double
+//
+
+max_convectivity_for_stratiform = 0.4;
+
+//======================================================================
+//
+// DETERMINING ADVANCED ECHO TYPE USING CLUMPING AND TEMPERATURE.
+//
+// We performing clumping on the convectivity field to identify 
+//   convective entities as objects. The main threshold used for the 
+//   clumping is min_convectivity_for_convective. By default a secondary 
+//   threshold is also used - see below.
+//
+//======================================================================
+ 
+///////////// clumping_use_dual_thresholds ////////////
+//
+// Option to use dual thresholds to better identify convective clumps.
+//
+// NOTE: this step is performed in 2D. If set, the clumping is performed 
+//   in two stages. First, an outer convectivity envelope is computed, 
+//   using min_convectivity_for_convective. Then, using the parameters 
+//   below, for each clump a search is performed for sub-clumps within the 
+//   envelope of the main clump, suing the secondary threshold. If there 
+//   is only one sub-clump, the original clump is used unchanged. If there 
+//   are two or more valid sub-clumps, based on the parameters below, 
+//   these sub-clumps are progrresively grown to where they meet, or to 
+//   the original clump envelope. The final 3D clumps are computed by 
+//   breaking the original clump into regions based upon these secondary 
+//   2D areas.
+//
+//
+// Type: boolean
+//
+
+clumping_use_dual_thresholds = TRUE;
+
+///////////// clumping_secondary_convectivity /////////
+//
+// Secondary convectivity threshold for clumping.
+//
+// We use the secondary threshold to find sub-clumps within the envelope 
+//   of each original clump.
+//
+//
+// Type: double
+//
+
+clumping_secondary_convectivity = 0.65;
+
+///////////// all_subclumps_min_area_fraction /////////
+//
+// Min area of all sub-clumps, as a fraction of the original clump area.
+//
+// We sum the areas of the sub-clumps, and compute the fraction relative 
+//   to the area of the original clump. For the sub-clumps to be valid, 
+//   the computed fraction must exceed this parameter.
+//
+//
+// Type: double
+//
+
+all_subclumps_min_area_fraction = 0.33;
+
+///////////// each_subclump_min_area_fraction /////////
+//
+// Min area of each valid sub-clump, as a fraction of the original 
+//   clump.
+//
+// We compute the area of each sub-clump, and compute the fraction 
+//   relative to the area of the original clump. For a subclump to be 
+//   valid, the area fraction must exceed this parameter.
+//
+//
+// Type: double
+//
+
+each_subclump_min_area_fraction = 0.02;
+
+///////////// each_subclump_min_area_km2 //////////////
+//
+// Min area of each valid sub-clump (km2).
+//
+// We compute the area of each sub-clump. For a subclump to be valid, 
+//   the area must exceed this parameter.
+//
+//
+// Type: double
+//
+
+each_subclump_min_area_km2 = 2;
+
+//======================================================================
+//
+// SPECIFYING VERTICAL LEVELS FOR ADVANCED ECHO TYPE - TEMPERATURE or 
+//   HEIGHT?.
+//
+// We need to specify the vertical separation between shallow, mid-level 
+//   and high clouds. We use the freezing level to separate warm clouds 
+//   and cold clouds. And we use the divergence level to separate the 
+//   mid-level clouds from high-level clouds such as anvil. These vertical 
+//   limits can be specified as heights MSL (in km), or as temperatures. 
+//   If temperatures are used, we read in the temperature profile from a 
+//   model.
+//
+//======================================================================
+ 
+///////////// vert_levels_type ////////////////////////
+//
+// How we specify the vertical levels.
+//
+// If temperatures are used, we need to read in the temperature profile 
+//   from a model.
+//
+//
+// Type: enum
+// Options:
+//     VERT_LEVELS_BY_TEMP
+//     VERT_LEVELS_BY_HT
+//
+
+vert_levels_type = VERT_LEVELS_BY_TEMP;
+
+///////////// temp_profile_url ////////////////////////
+//
+// URL for temperature profile data, in MDV/Netcdf-CF format.
+//
+// We read in the model data that is closest in time to the reflectivity 
+//   data.
+//
+//
+// Type: string
+//
+
+temp_profile_url = "$(NEXRAD_DATA_DIR)/mdv/ruc";
+
+///////////// temp_profile_field_name /////////////////
+//
+// Name of temperature field in the model data. This should be in 
+//   degrees C.
+//
+//
+// Type: string
+//
+
+temp_profile_field_name = "TMP";
+
+///////////// temp_profile_search_margin //////////////
+//
+// Search margin for finding the temp profile data (secs).
+//
+// The temp profile must be within this number of seconds of the dbz 
+//   data.
+//
+//
+// Type: int
+//
+
+temp_profile_search_margin = 21600;
+
+///////////// shallow_threshold_temp //////////////////
+//
+// Shallow cloud temperature threshold (degC).
+//
+// Shallow cloud tops are below this temperature. Used if 
+//   vert_levels_type = VERT_LEVELS_BY_TEMP.
+//
+//
+// Type: double
+//
+
+shallow_threshold_temp = 0;
+
+///////////// deep_threshold_temp /////////////////////
+//
+// Deep cloud temperature threshold (degC).
+//
+// Deep clouds extend above this height. Used if vert_levels_type = 
+//   VERT_LEVELS_BY_TEMP.
+//
+//
+// Type: double
+//
+
+deep_threshold_temp = -25;
+
+///////////// shallow_threshold_ht ////////////////////
+//
+// Shallow cloud height threshold if temperature is not available (km).
+//
+// Shallow cloud tops are below this height. Used if vert_levels_type = 
+//   VERT_LEVELS_BY_HT.
+//
+//
+// Type: double
+//
+
+shallow_threshold_ht = 4;
+
+///////////// deep_threshold_ht ///////////////////////
+//
+// Deep cloud height threshold if temperature is not available (km).
+//
+// Deep clouds extend above this height. Used if vert_levels_type = 
+//   VERT_LEVELS_BY_HT.
+//
+//
+// Type: double
+//
+
+deep_threshold_ht = 9;
+
+//======================================================================
+//
+// DETERMINING ADVANCED CATEGORY FROM CLUMP PROPERTIES.
+//
+// Based on the temp or height criteria above, we compute the deep, mid 
+//   and shallow convective fractions within each sub-clump. We also 
+//   determine whether there is stratiform echo below the clump. The 
+//   following parameters are then used to determine the deep, elevated, 
+//   mid or shallow echo types for the convection. If a determination is 
+//   not clear, the overall category is set to mixed.
+//
+//======================================================================
+ 
+///////////// min_conv_fraction_for_deep //////////////
+//
+// The minimun convective fraction in the clump for deep convection.
+//
+// The fraction of deep within the clump must exceed this for an echo 
+//   type of deep.
+//
+//
+// Type: double
+//
+
+min_conv_fraction_for_deep = 0.05;
+
+///////////// min_conv_fraction_for_shallow ///////////
+//
+// The minimun convective fraction in the clump for shallow convection.
+//
+// The fraction of shallow within the clump must exceed this for an echo 
+//   type of shallow.
+//
+//
+// Type: double
+//
+
+min_conv_fraction_for_shallow = 0.95;
+
+///////////// max_shallow_conv_fraction_for_elevated //
+//
+// The maximum shallow convective fraction in the clump for elevated 
+//   convection.
+//
+// The fraction of shallow within the clump must be less than this for 
+//   an echo type of elevated.
+//
+//
+// Type: double
+//
+
+max_shallow_conv_fraction_for_elevated = 0.05;
+
+///////////// max_deep_conv_fraction_for_elevated /////
+//
+// The maximum deep convective fraction in the clump for elevated 
+//   convection.
+//
+// The fraction of deep within the clump must be less than this for an 
+//   echo type of elevated.
+//
+//
+// Type: double
+//
+
+max_deep_conv_fraction_for_elevated = 0.25;
+
+///////////// min_strat_fraction_for_strat_below //////
+//
+// The minimun area fraction of stratiform echo below the clump to 
+//   determine there is stratiform below.
+//
+// For elevated convection, we need to determine if there is stratiform 
+//   echo below. For a designation of elevated, this is the minimum 
+//   fraction of the area below the clump that has stratiform echo in the 
+//   plane immediately below it.
+//
+//
+// Type: double
+//
+
+min_strat_fraction_for_strat_below = 0.9;
+
+//======================================================================
+//
+// DATA OUTPUT.
+//
+//======================================================================
+ 
+///////////// output_url //////////////////////////////
+//
+// Output URL.
+//
+// Output files are written to this URL.
+//
+//
+// Type: string
+//
+
+output_url = "$(NEXRAD_DATA_DIR)/mdv/ecco/mosaic";
+
+///////////// write_partition /////////////////////////
+//
+// Write out partition fields.
+//
+// This will write out the 3D, 2D and column-max partition.
+//
+//
+// Type: boolean
+//
+
+write_partition = TRUE;
+
+///////////// write_texture ///////////////////////////
+//
+// Write out texture fields.
+//
+// This will write out the 3D and column-max texture.
+//
+//
+// Type: boolean
+//
+
+write_texture = TRUE;
+
+///////////// write_convectivity //////////////////////
+//
+// Write out convectivity fields.
+//
+// This will write out the 3D and column-max convectivity.
+//
+//
+// Type: boolean
+//
+
+write_convectivity = TRUE;
+
+///////////// write_3D_dbz ////////////////////////////
+//
+// Write out 3D dbz field.
+//
+// This will be an echo of the input field.
+//
+//
+// Type: boolean
+//
+
+write_3D_dbz = TRUE;
+
+///////////// write_col_max_dbz ///////////////////////
+//
+// Write out column maximum dbz field.
+//
+// This is the max reflectivity at any height.
+//
+//
+// Type: boolean
+//
+
+write_col_max_dbz = TRUE;
+
+///////////// write_convective_dbz ////////////////////
+//
+// Write out convective dbz field.
+//
+// This will write out the 3D convective DBZ field.
+//
+//
+// Type: boolean
+//
+
+write_convective_dbz = TRUE;
+
+///////////// write_tops //////////////////////////////
+//
+// Write out echo, convective and stratiform tops.
+//
+// These are 2D fields.
+//
+//
+// Type: boolean
+//
+
+write_tops = TRUE;
+
+///////////// write_fraction_active ///////////////////
+//
+// Write out 2D field showing fraction active.
+//
+// This the active fraction in the computational circle.
+//
+//
+// Type: boolean
+//
+
+write_fraction_active = TRUE;
+
+///////////// write_height_grids //////////////////////
+//
+// Write out 2D field showing shallow and deep heights.
+//
+// These are based on model temperature.
+//
+//
+// Type: boolean
+//
+
+write_height_grids = FALSE;
+
+///////////// write_temperature ///////////////////////
+//
+// Write out 3D temperature field.
+//
+// This comes from a model, remapped onto the reflectivity grid.
+//
+//
+// Type: boolean
+//
+
+write_temperature = TRUE;
+
+///////////// write_clumping_debug_fields /////////////
+//
+// Option to write fields to the output files for debugging the dual 
+//   threshold clumping.
+//
+// If this is set, the following debug fields are written to the output 
+//   files: .
+//
+//
+// Type: boolean
+//
+
+write_clumping_debug_fields = FALSE;
+
+
+
+
+
+
+

Run Ecco

+

Ecco computes the convective/stratiform partition using radar reflectivity in Cartesian coordinates.

+

We run Ecco by specifying the parameter file, and the start and end times for the analysis.

+
+
+
# Run Ecco using param file
+!/usr/local/lrose/bin/Ecco -params ./params/Ecco.nexrad_mosaic -debug -start "2021 07 06 22 00 00" -end "2021 07 06 22 30 00"
+
+
+
+
+
======================================================================
+Program 'Ecco'
+Run-time 2024/02/21 08:18:38.
+
+Copyright (c) 1992 - 2024
+University Corporation for Atmospheric Research (UCAR)
+National Center for Atmospheric Research (NCAR)
+Boulder, Colorado, USA.
+
+Redistribution and use in source and binary forms, with
+or without modification, are permitted provided that the following
+conditions are met:
+
+1) Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2) Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+
+3) Neither the name of UCAR, NCAR nor the names of its contributors, if
+any, may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+4) If the software is modified to produce derivative works, such modified
+software should be clearly marked, so as not to confuse it with the
+version available from UCAR.
+
+======================================================================
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220000.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_220000.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_220500.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_220500.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221000.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_221000.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_221500.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_221500.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222000.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_222000.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_222500.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_222500.mdv.cf.nc
+
+
+
Read in file: /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/mosaic/20210706/20210706_223000.mdv.cf.nc
+
+
+
Wrote file: /tmp/lrose_data/nexrad_mosaic/mdv/ecco/mosaic/20210706/20210706_223000.mdv.cf.nc
+
+
+
+
+
+
+

Read in Ecco results for a selected time

+
+
+
# Read in ecco results for a selected time
+filePathEcco = os.path.join(nexradDataDir, 'mdv/ecco/mosaic/20210706/20210706_221500.mdv.cf.nc')
+dsEcco = nc.Dataset(filePathEcco)
+print(dsEcco)
+
+# create 3D ecco type array with nans for missing vals
+
+eccoField = dsEcco['EchoType3D']
+ecco3D = np.array(eccoField)
+fillValue = eccoField._FillValue
+print("fillValue: ", fillValue)
+print("ecco3D.shape: ", ecco3D.shape)
+
+# if 4D (i.e. time is dim0) change to 3D
+if (len(ecco3D.shape) == 4):
+    ecco3D = ecco3D[0]
+
+# Compute Ecco grid limits
+(nZEcco, nYEcco, nXEcco) = ecco3D.shape
+lon = np.array(dsEcco['x0'])
+lat = np.array(dsEcco['y0'])
+ht = np.array(dsEcco['z2'])
+dLonEcco = lon[1] - lon[0]
+dLatEcco = lat[1] - lat[0]
+minLonEcco = lon[0] - dLonEcco / 2.0
+maxLonEcco = lon[-1] + dLonEcco / 2.0
+minLatEcco = lat[0] - dLatEcco / 2.0
+maxLatEcco = lat[-1] + dLatEcco / 2.0
+minHtEcco = ht[0]
+maxHtEcco = ht[-1]
+print("minLonEcco, maxLonEcco: ", minLonEcco, maxLonEcco)
+print("minLatEcco, maxLatEcco: ", minLatEcco, maxLatEcco)
+print("minHt, maxHt: ", minHtEcco, maxHtEcco)
+print("ht: ", ht)
+del lon, lat, ht
+
+
+
+
+
<class 'netCDF4._netCDF4.Dataset'>
+root group (NETCDF4 data model, file format HDF5):
+    Conventions: CF-1.6
+    history: Converted from NetCDF to MDV, 2024/02/21 08:19:44
+  Ncf:history: Data merged from following files:
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KGLD/20210706/ncf_20210706_221420.nc
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KUEX/20210706/ncf_20210706_221633.nc
+  /tmp/lrose_data/nexrad_mosaic/mdv/radarCart/KDDC/20210706/ncf_20210706_221600.nc
+
+  Ncf:comment: 
+ : Stratfinder used to identify stratiform regions
+    source: NEXRAD radars
+    title: 3D RADAR MOSAIC
+    comment: 
+    dimensions(sizes): time(1), bounds(2), x0(900), y0(700), x1(139), y1(72), z0(1), z1(32), z2(31), nbytes_mdv_chunk_0000(485)
+    variables(dimensions): float64 time(time), float64 start_time(time), float64 stop_time(time), float64 time_bounds(time, bounds), float32 x0(x0), float32 y0(y0), float32 x1(x1), float32 y1(y1), float32 z0(z0), float32 z1(z1), float32 z2(z2), int32 grid_mapping_0(), int32 grid_mapping_1(), int32 mdv_master_header(time), int8 mdv_chunk_0000(time, nbytes_mdv_chunk_0000), int16 DbzTextureComp(time, z0, y0, x0), int16 ConvectivityComp(time, z0, y0, x0), int16 DbzComp(time, z0, y0, x0), int16 FractionActive(time, z0, y0, x0), int16 ConvTops(time, z0, y0, x0), int16 StratTops(time, z0, y0, x0), int16 EchoTops(time, z0, y0, x0), int16 TMP(time, z1, y1, x1), int8 EchoTypeComp(time, z0, y0, x0), int16 DbzConv(time, z2, y0, x0), int16 DbzTexture3D(time, z2, y0, x0), int16 Convectivity3D(time, z2, y0, x0), int16 Dbz3D(time, z2, y0, x0), int8 EchoType3D(time, z2, y0, x0)
+    groups: 
+
+
+
fillValue:  -128
+ecco3D.shape:  (1, 31, 700, 900)
+minLonEcco, maxLonEcco:  -104.50500106811523 -95.50500106811523
+minLatEcco, maxLatEcco:  35.4950008392334 42.49499702453613
+minHt, maxHt:  1.25 20.0
+ht:  [ 1.25  1.5   1.75  2.    2.25  2.5   2.75  3.    3.5   4.    4.5   5.
+  5.5   6.    6.5   7.    7.5   8.    8.5   9.   10.   11.   12.   13.
+ 14.   15.   16.   17.   18.   19.   20.  ]
+
+
+
+
+
+
+

Compute the column-max of the Ecco 3-D results

+
+
+
# Compute column-max echo type
+eccoPlaneMax = np.amax(ecco3D, (0))
+eccoPlaneMax[eccoPlaneMax == fillValue] = np.nan
+print(eccoPlaneMax.shape)
+print(eccoPlaneMax[eccoPlaneMax != np.nan])
+print(np.min(eccoPlaneMax[eccoPlaneMax != np.nan]))
+print(np.max(eccoPlaneMax))
+
+
+
+
+
(700, 900)
+[nan nan nan ... nan nan nan]
+nan
+nan
+
+
+
+
+
+
+

Plot the column max of the Ecco results

+
+
+
# Plot column-max ecco
+figEcco = plt.figure(figsize=(8, 8), dpi=150)
+axEcco = new_map(figEcco)
+plt.imshow(eccoPlaneMax,
+            cmap='rainbow', vmin=12, vmax=40,
+            interpolation = 'bilinear',
+            origin = 'lower',
+            extent = (minLonEcco, maxLonEcco, minLatEcco, maxLatEcco))
+axEcco.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='black')
+axEcco.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')
+cbarEcco = plt.colorbar(label="Echo type", cax=None, orientation="vertical", shrink=0.6)
+cbarEcco.set_ticks([14,16,18,25,32,34,36,38],
+                   labels=['StratLowLow', 'StratMid', 'StratHigh', 'Mixed',
+                           'ConvElev', 'ConvLow', 'ConvMid', 'ConvDeep'])
+
+plt.title("Column max of Echo Type for radar mosaic: " + startTimeStr)
+
+
+
+
+
Text(0.5, 1.0, 'Column max of Echo Type for radar mosaic: 2021/07/06-22:14:20 UTC')
+
+
+../../_images/nexrad_mosaic.erad_tutorial_55_1.png +
+
+
+
+
+

Plot vertical sections for Echo Type

+
+
+
# Compute W-E Ecco vertical section
+nYHalfEcco = int(nYEcco/2)
+eccoVertWE = ecco3D[:, nYHalfEcco:(nYHalfEcco+1), :]
+eccoVertWE = eccoVertWE.reshape(eccoVertWE.shape[0], eccoVertWE.shape[2])
+print('eccoVertWE.shape: ', eccoVertWE.shape)
+eccoVertWE[eccoVertWE == fillValue] = np.nan
+eccoVertWEMax = np.amax(ecco3D, axis=1)
+eccoVertWEMax[eccoVertWEMax == fillValue] = np.nan
+print('eccoVertWEMax.shape: ', eccoVertWEMax.shape)
+
+# Compute ecco N-S vertical section
+nXHalfEcco = int(nXEcco/2)
+eccoVertNS = ecco3D[:, :, nXHalfEcco:(nXHalfEcco+1)]
+eccoVertNS = eccoVertNS.reshape(eccoVertNS.shape[0], eccoVertNS.shape[1])
+eccoVertNS[eccoVertNS == fillValue] = np.nan
+print('eccoVertNS.shape: ', eccoVertNS.shape)
+eccoVertNSMax = np.amax(ecco3D, axis=2)
+eccoVertNSMax[eccoVertNSMax == fillValue] = np.nan
+print('eccoVertNSMax.shape: ', eccoVertNSMax.shape)
+
+
+
+
+
eccoVertWE.shape:  (31, 900)
+eccoVertWEMax.shape:  (31, 900)
+eccoVertNS.shape:  (31, 700)
+eccoVertNSMax.shape:  (31, 700)
+
+
+
+
+
+
+
# Plot mid W-E ecco vertical section
+
+figEccoVert = plt.figure(num=2, figsize=[12, 8], layout='constrained')
+axEv1 = figEccoVert.add_subplot(211, xlim = (minLonEcco, maxLonEcco), ylim = (minHtEcco, maxHtEcco))
+plt.imshow(eccoVertWE,
+    vmin=12, vmax=40,
+    cmap='rainbow',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLonEcco, maxLonEcco, minHtEcco, maxHtEcco))
+axEv1.set_aspect(0.15)
+axEv1.set_xlabel('Longitude (deg)')
+axEv1.set_ylabel('Height (km)')
+plt.title("Echo type vert slice mid W-E: " + startTimeStr)
+
+cbar = plt.colorbar(label="ecco", cax=None, orientation="vertical", shrink=1.5)
+cbar.set_ticks([14,16,18,25,32,34,36,38], labels=['StratLowLow', 'StratMid', 'StratHigh', 'Mixed', 'ConvElev', 'ConvLow', 'ConvMid', 'ConvDeep'])
+
+# Plot mid N-S ecco vertical section
+
+axEv2 = figEccoVert.add_subplot(212, xlim = (minLatEcco, maxLatEcco), ylim = (minHtEcco, maxHtEcco))
+plt.imshow(eccoVertNS,
+    vmin=12, vmax=40,
+    cmap='rainbow',
+    interpolation = 'bilinear',
+    origin = 'lower',
+    extent = (minLatEcco, maxLatEcco, minHtEcco, maxHtEcco))
+axEv2.set_aspect(0.15)
+axEv2.set_xlabel('Latitude (deg)')
+axEv2.set_ylabel('Height (km)')
+plt.title("Echo type vert slice mid NS: " + startTimeStr)
+
+
+
+
+
Text(0.5, 1.0, 'Echo type vert slice mid NS: 2021/07/06-22:14:20 UTC')
+
+
+../../_images/nexrad_mosaic.erad_tutorial_58_1.png +
+
+
+ + + + +
+ +
+ + +
+

previous

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+
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next

+

Environment overview

+
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+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/notebook-template.html b/_preview/104/notebooks/notebook-template.html new file mode 100644 index 00000000..3277eb74 --- /dev/null +++ b/_preview/104/notebooks/notebook-template.html @@ -0,0 +1,834 @@ + + + + + + + + Project Pythia Notebook Template — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
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Let’s start here! If you can directly link to an image relevant to your notebook, such as canonical logos, do so here at the top of your notebook. You can do this with Markdown syntax,

+
+

![<image title>](http://link.com/to/image.png "image alt text")

+
+

or edit this cell to see raw HTML img demonstration. This is preferred if you need to shrink your embedded image. Either way be sure to include alt text for any embedded images to make your content more accessible.

+

Project Pythia Logo

+
+

Project Pythia Notebook Template

+

Next, title your notebook appropriately with a top-level Markdown header, #. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a --- cell to visually distinguish the transition to the prerequisites section.

+
+
+

Overview

+

If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,

+
    +

  1. This is a numbered list of the specific topics

    2. +

  2. These should map approximately to your main sections of content

    3. +

  3. Or each second-level, ##, header in your notebook

    4. +

  4. Keep the size and scope of your notebook in check

    5. +

  5. And be sure to let the reader know up front the important concepts they’ll be leaving with

    6. +
+
+
+

Prerequisites

+

This section was inspired by this template of the wonderful The Turing Way Jupyter Book.

+

Following your overview, tell your reader what concepts, packages, or other background information they’ll need before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with | vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.

+

Label the importance of each concept explicitly as helpful/necessary.

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Intro to Cartopy

Necessary

Understanding of NetCDF

Helpful

Familiarity with metadata structure

Project management

Helpful

+
    +

  • Time to learn: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.

    • +

  • System requirements:

    +
      +

    • Populate with any system, version, or non-Python software requirements if necessary

      • +

    • Otherwise use the concepts table above and the Imports section below to describe required packages as necessary

      • +

    • If no extra requirements, remove the System requirements point altogether

      • +
    +
    • +
+
+
+
+

Imports

+

Begin your body of content with another --- divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports up-front:

+
+
+
import sys
+
+
+
+
+
+
+

Your first content section

+

This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,

+
+
+
# as well as any and all of your code cells
+print("Hello world!")
+
+
+
+
+
Hello world!
+
+
+
+
+
+

A content subsection

+

Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!

+
+
+
# some subsection code
+new = "helpful information"
+
+
+
+
+
+
+

Another content subsection

+

Keep up the good work! A note, try to avoid using code comments as narrative, and instead let them only exist as brief clarifications where necessary.

+
+
+
+

Your second content section

+

Here we can move on to our second objective, and we can demonstrate

+
+

Subsection to the second section

+
+

a quick demonstration

+
+
of further and further
+
+
header levels
+

as well \(m = a * t / h\) text! Similarly, you have access to other \(\LaTeX\) equation functionality via MathJax (demo below from link),

+
+()\[\begin{align} +\dot{x} & = \sigma(y-x) \\ +\dot{y} & = \rho x - y - xz \\ +\dot{z} & = -\beta z + xy +\end{align}\]
+

Check out any number of helpful Markdown resources for further customizing your notebooks and the Jupyter docs for Jupyter-specific formatting information. Don’t hesitate to ask questions if you have problems getting it to look just right.

+
+
+
+
+
+
+

Last Section

+

If you’re comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):

+
+

Info

+

Your relevant information here!

+
+

Feel free to copy this around and edit or play around with yourself. Some other admonitions you can put in:

+
+

Success

+

We got this done after all!

+
+
+

Warning

+

Be careful!

+
+
+

Danger

+

Scary stuff be here.

+
+

We also suggest checking out Jupyter Book’s brief demonstration on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to customize how your code content is displayed and even demonstrate errors without altogether crashing our loyal army of machines!

+
+
+
+

Summary

+

Add one final --- marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.

+
+

What’s next?

+

Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!

+
+
+
+

Resources and references

+

Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you’re done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:

+
    +

  • Kernel > Restart Kernel and Run All Cells... to confirm that your notebook will cleanly run from start to finish

    • +

  • Kernel > Restart Kernel and Clear All Outputs... before committing your notebook, our machines will do the heavy lifting

    • +

  • Take credit! Provide author contact information if you’d like; if so, consider adding information here at the bottom of your notebook

    • +

  • Give credit! Attribute appropriate authorship for referenced code, information, images, etc.

    • +

  • Only include what you’re legally allowed: no copyright infringement or plagiarism

    • +
+

Thank you for your contribution!

+
+
+ + + + +
+ +
+
+
+
+
+ +
+
+ + + +
+
+ +
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+
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+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/answer_question_pyart_meteoswiss.html b/_preview/104/notebooks/pyart/answer_question_pyart_meteoswiss.html new file mode 100644 index 00000000..6d4d8b8b --- /dev/null +++ b/_preview/104/notebooks/pyart/answer_question_pyart_meteoswiss.html @@ -0,0 +1,718 @@ + + + + + + + + Exercice Sample Solution — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+
+
+ +
+ +
+

Exercice Sample Solution

+

Load required libraries

+
+
+
import pyart
+pyart.config.load_config('mch_config.py')
+import numpy as np
+import cartopy.crs as ccrs
+import cartopy
+import matplotlib.pyplot as plt
+import glob
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
    +

  1. Load all radar files in /data/question_pyart_meteoswiss and merge them into one single radar object

    2. +
+
+
+
files_radar = sorted(glob.glob('./data/question_pyart_meteoswiss/MLA211941205*'))
+for i,f in enumerate(files_radar):
+    radar = pyart.io.read_cfradial(f)
+  
+    if i == 0:
+        radar_merged = radar
+    else:
+        radar_merged = pyart.util.join_radar(radar_merged, 
+                                       radar)
+
+
+
+
+
    +

  1. Perform attenuation correction of ZH, using a constant freezing level height of 2700 m.

    2. +
+
+
+
# Compute attenuation
+out = pyart.correct.calculate_attenuation_zphi(radar_merged, fzl = 4200,
+                           phidp_field = 'uncorrected_differential_phase',
+                           temp_ref = 'fixed_fzl')
+spec_at, pia, cor_z, spec_diff_at, pida, cor_zdr = out
+radar_merged.add_field('corrected_reflectivity', cor_z)
+
+
+
+
+
    +

  1. Estimate the QPE with a a polynomial Z-R relation.

    2. +
+
+
+
qpe = pyart.retrieve.est_rain_rate_zpoly(radar_merged, refl_field = 'corrected_reflectivity')
+
+radar_merged.add_field('radar_estimated_rain_rate', qpe)
+
+
+
+
+
    +

  1. Compute a CAPPI of the resulting radar estimate rain rate from 500 to 8000 m above the radar using a vertical resolution of 100 m and a horizontal resolution of 500 m at a x and y distance of up to 100 km to the radar.

    2. +
+
+
+
zmin = 500
+zmax = 8000
+ymin= xmin = -100000
+ymax = xmax = 100000
+lat = float(radar.latitude['data'])
+lon = float(radar.longitude['data'])
+alt = float(radar.altitude['data'])
+# number of grid points in cappi
+cappi_res_h = 500
+cappi_res_v = 100
+ny = int((ymax-ymin)/cappi_res_h)+1
+nx = int((xmax-xmin)/cappi_res_h)+1
+nz = int((zmax-zmin)/cappi_res_v)+1
+
+cappi_qpe = pyart.map.grid_from_radars(radar_merged, grid_shape=(nz, ny, nx),
+        grid_limits=((zmin, zmax), (ymin, ymax),
+                     (xmin, xmax)),
+        fields=['radar_estimated_rain_rate'])
+
+
+
+
+
    +

  1. Using numpy, perform a weighted average of all CAPPI levels using the weights. Finally display the results.

    2. +
+
+
+
weighting = np.exp(-0.5*  cappi_qpe.z['data'] / 1000)
+
+qpe_ground = weighting[:,None,None]*cappi_qpe.fields['radar_estimated_rain_rate']['data']
+qpe_ground = np.nansum(qpe_ground, axis = 0) / np.sum(weighting)
+
+plt.pcolormesh(cappi_qpe.point_longitude['data'][0], 
+               cappi_qpe.point_latitude['data'][0],
+               qpe_ground, vmax = 15)
+plt.colorbar(label = 'Estimated rain rate at ground [mm/h]')
+
+
+
+
+
/tmp/ipykernel_1060/2553980288.py:6: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
+  plt.pcolormesh(cappi_qpe.point_longitude['data'][0],
+
+
+
<matplotlib.colorbar.Colorbar at 0x7f45d7c4a220>
+
+
+../../_images/answer_question_pyart_meteoswiss_12_2.png +
+
+

Now let’s compare this QPE with the operational QPE of MeteoSwiss at the same timestep:

+qpe_op.png +

The agreement near the radar is not too bad, even with such a simple aggregation method. In the south we see some large discrepancies. This is due to the fact that the operational QPE includes many additional steps. In this case, the difference is likely due to the correction for partial beam blocking that is performed by the operational QPE in this mountaineous region south of the radar.

+
+ + + + +
+ +
+ + +
+

previous

+

Exercice

+
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next

+

Wradlib Tutorial

+
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+
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+
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+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/exercice1_swiss_thunderstorm.html b/_preview/104/notebooks/pyart/exercice1_swiss_thunderstorm.html new file mode 100644 index 00000000..7e42b295 --- /dev/null +++ b/_preview/104/notebooks/pyart/exercice1_swiss_thunderstorm.html @@ -0,0 +1,946 @@ + + + + + + + + Filtering and retrievals on raw Swiss C-band data — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
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+

Filtering and retrievals on raw Swiss C-band data

+

In this exercice we will load raw unfiltered Swiss C-band data during a thunderstorm event and process it to ultimately estimate the precipitation intensities. The following topics will be tackled.

+
    +

  • Ground clutter detection

    • +

  • Attenuation correction

    • +

  • KDP estimation

    • +

  • Hydrometeor classification

    • +

  • QPE

    • +
+
+
+
# Imports
+
+import numpy as np
+import cartopy.crs as ccrs
+import cartopy
+import matplotlib.pyplot as plt
+
+import pyart
+pyart.config.load_config('mch_config.py')
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+

Note that you can create your own Py-ART configuration file, which defines default field names, default colormaps, limits, and much more. This is the one we use at MeteoSwiss. You can then either load it at startup in your python code or define the environment variable PYART_CONFIG to point to your file in your work environment.

+
+

Reading the data

+

Let’s start by loading our radar file which is of the standard CFRadial type. It corresponds to the third sweep of the operational radar scans, which is a PPI at 1° elevation. It contains raw radar data (before pre-processing) at a resolution of 83 m. We then add the temperature obtained from the COSMO NWP model to our radar object (note that this temperature was previously interpolated from the model grid to the radar polar grid). Note that the freezing level is quite high in this example (around 4200 m.).

+
+
+
# Open radar file
+file_radar = './data/exercice1_swiss_thunderstorm/MHL2217907250U.003.nc'
+radar = pyart.io.read_cfradial(file_radar)
+
+# Add temperature
+temp = pyart.io.read_cfradial('./data/exercice1_swiss_thunderstorm/20220628073500_savevol_COSMO_LOOKUP_TEMP.nc')
+radar.add_field('temperature', temp.fields['temperature'])
+
+
+
+
+
+
+

Ground-clutter and noise removal

+

Py-ART uses gatefilters which are a kind of mask to filter out problematic measurements. Most processing routines can take a gatefilter as input and will ignore pixels that were filtered out.

+

Here we create a gate filter based on the radar moments and their texture to filter out noise and ground clutter. Since we are interested in a strong thunderstorm we also extend this filter to remove all measurements with a SNR ratio of less than 10 dB.

+
+
+
gtfilter = pyart.filters.moment_and_texture_based_gate_filter(radar)
+gtfilter.exclude_below('signal_to_noise_ratio', 10)
+
+
+
+
+

Let’s compare visually the reflectivity before and after filtering. Note that the plot function of Py-ART take a gatefilter as input.

+
+
+
fig, ax = plt.subplots(1,2, figsize=(10,6), sharex= True, sharey=True)
+display = pyart.graph.RadarDisplay(radar)
+display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., ax = ax[0], colorbar_label = 'Raw')
+display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., gatefilter = gtfilter, 
+                 ax = ax[1], colorbar_label = 'Filtered')
+ax[0].set_xlim([-50,50])
+ax[0].set_ylim([-50,50])
+ax[0].set_aspect('equal', 'box')
+ax[1].set_aspect('equal', 'box')
+
+
+
+
+../../_images/exercice1_swiss_thunderstorm_8_0.png +
+
+

Here it is clear that most ground clutter (mostly north west and east of the radar), as well as noise have been filtered out.

+
+
+

Attenuation correction

+

We can expect strong attenuation behind a thunderstorm like this. So it is a good idea to try to correct for it. Knowledge of the specific attenuation can also be very insightful.

+
+
+
out = pyart.correct.calculate_attenuation_zphi(radar, fzl = 4200,
+                           gatefilter=gtfilter,
+                           phidp_field = 'uncorrected_differential_phase',
+                           temp_field = 'temperature',
+                           temp_ref = 'temperature')
+spec_at, pia, cor_z, spec_diff_at, pida, cor_zdr = out
+radar.add_field('corrected_reflectivity', cor_z)
+radar.add_field('corrected_differential_reflectivity', cor_zdr)
+radar.add_field('specific_attenuation', spec_at)
+
+
+
+
+

Here we use the Z-PHI method, which uses the relation between differential phase shift and specific attenuation. However it works only in the liquid phase. So you need to provide it either with a fixed freezing level height, a field of freezing level heights or a field of temperature. Here we provide the later.

+

This method provides us with 5 output variables

+
    +

  • specific attenuation dB/km

    • +

  • path integrated attenuation dB

    • +

  • corrected reflectivity dBZ

    • +

  • differential specific attenuation dB

    • +

  • path integrated differential attenuation dB

    • +

  • corrected differential reflectivity (ZDR) dB

    • +
+

We will now plot the specific attenuation as well as the raw and corrected reflectivities.

+
+
+
fig, ax = plt.subplots(1,3, figsize=(16,6), sharex= True, sharey=True)
+display = pyart.graph.RadarDisplay(radar)
+display.plot_ppi('specific_attenuation', 0, vmin=0, vmax=1.5, gatefilter = gtfilter,
+                     ax = ax[0])
+display.plot_ppi('reflectivity', 0, vmin=0, vmax=60., ax = ax[1],  gatefilter = gtfilter,
+                 colorbar_label = 'ZH with attenuation [dBZ]')
+display.plot_ppi('corrected_reflectivity', 0, vmin=0, vmax=60., gatefilter = gtfilter,
+                     ax = ax[2], colorbar_label = 'ZH attenuation corrected [dBZ]')
+ax[0].set_xlim([-50,50])
+ax[0].set_ylim([-50,50])
+ax[0].set_aspect('equal', 'box')
+ax[1].set_aspect('equal', 'box')
+ax[2].set_aspect('equal', 'box')
+
+
+
+
+../../_images/exercice1_swiss_thunderstorm_13_0.png +
+
+

We can clearly observe a strong specific attenuation within the thunderstorm as well as a significant difference in reflectivity before/after correction behind the thunderstorm to the west.

+
+
+

KDP estimation

+

Another very interesting radar variable is the specific differential phase shift KDP. Large KDP indicates the presence of large oblate drops and is linked to very strong precipitation. KDP is also needed for the hydrometeor classification algorithm. However KDP is not measured directly and needs to be estimated numerically from the raw differential phase shift (PHIDP). Py-ART provides three different retrieval methods. We will use the method by Maesaka et al. (2012) which is fast and robust but assumes KDP to be positive and is therefore limited to rainfall below the melting layer and/or warm clouds.

+
+
+
kdp, _, _ = pyart.retrieve.kdp_maesaka(radar, gatefilter = gtfilter,
+                                       psidp_field = 'uncorrected_differential_phase')
+radar.add_field('specific_differential_phase', kdp)
+
+fig, ax = plt.subplots(1,1, figsize=(6,6))
+display = pyart.graph.RadarDisplay(radar)
+display.plot_ppi('specific_differential_phase', 0, vmin = 0, vmax = 10,
+                 ax = ax,  gatefilter = gtfilter)
+
+ax.set_xlim([-50,50])
+ax.set_ylim([-50,50])
+ax.set_aspect('equal', 'box')
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/core/fromnumeric.py:758: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
+  a.partition(kth, axis=axis, kind=kind, order=order)
+
+
+../../_images/exercice1_swiss_thunderstorm_16_1.png +
+
+

A look at the KDP field shows clusters of very large KDP (> 5 °/km) at the center of the thunderstorm.

+
+
+

Hydrometeor classification

+

The hydrometeor classification algorithm in Py-ART by Besic et al. (2016) uses ZH, ZDR, RHOHV, KDP and the temperature to classify hydrometeors into one of 8 classes:

+
    +

  • Ice hail, high density Graupel

    • +

  • Melting hail

    • +

  • Wet snow

    • +

  • Vertically oriented ice

    • +

  • Rain

    • +

  • Rimed particles

    • +

  • Light rain

    • +

  • Crystals

    • +

  • Aggregates

    • +
+

This algorithm requires centroids of polarimetric variables for the different hydrometeor classes. Below we provide it with centroids specifically suited for the radar of Monte Lema. If left empty, the algorithm will use default centroids at the right frequency band (X, C or S).

+
+
+
centroids = np.array([[13.8231,0.2514,0.0644,0.9861,1380.6],
+[3.0239,0.1971,0.,0.9661,1464.1],
+[4.9447,0.1142,0.,0.9787,-974.7],
+[34.2450,0.5540,0.1459,0.9937,945.3],
+[40.9432,1.0110,0.5141,0.9928,-993.5],
+[3.5202,-0.3498,0.,0.9746,843.2],
+[32.5287,0.9751,0.2640,0.9804,-55.5],
+[52.6547,2.7054,2.5101,0.9765,-1114.6],
+[46.4998,0.1978,0.6431,0.9845,1010.1]])
+
+
+hydro = pyart.retrieve.hydroclass_semisupervised(radar, mass_centers = centroids,
+                                 refl_field =  'corrected_reflectivity',
+                                 zdr_field = 'corrected_differential_reflectivity',
+                                 kdp_field = 'specific_differential_phase',
+                                 rhv_field = 'uncorrected_cross_correlation_ratio',
+                                 temp_field = 'temperature')
+
+radar.add_field('radar_echo_classification', hydro)
+
+fig, ax = plt.subplots(1,1, figsize=(6,6))
+display = pyart.graph.RadarDisplay(radar)
+import matplotlib as mpl
+
+labels = ['NC','AG', 'CR', 'LR', 'RP', 'RN', 'VI', 'WS', 'MH', 'IH/HDG']
+ticks = np.arange(len(labels))
+boundaries = np.arange(-0.5, len(labels) )
+norm = mpl.colors.BoundaryNorm(boundaries, 256)
+
+cax = display.plot_ppi('radar_echo_classification', 0, ax = ax,  gatefilter = gtfilter,
+                 norm = norm, ticks = ticks, ticklabs = labels)
+
+ax.set_xlim([-50,50])
+ax.set_ylim([-50,50])
+ax.set_aspect('equal', 'box')
+
+
+
+
+../../_images/exercice1_swiss_thunderstorm_19_0.png +
+
+

Note that the plotting commands are slightly more complicated due to the categorical colormap.

+

A look at the hydrometeor classification reveals the presence of wet hail in the center of the thunderstorm surrounded by rain and by light rain. A few isolated pixels (unfiltered ground clutter) are also classified as hail. +There was indeed intense hail at the ground on that day.

+
+
+

QPE

+

Py-ART provides several QPE algorithms but the most refined relies on the hydrometeor classification and uses different relations between radar variables and precipitation intensities within the different hydrometeor classes.

+
+
+
qpe = pyart.retrieve.est_rain_rate_hydro(radar, refl_field = 'corrected_reflectivity',
+                                         hydro_field = 'radar_echo_classification',
+                                         a_field = 'specific_attenuation',
+                                         thresh=40)
+
+radar.add_field('radar_estimated_rain_rate', qpe)
+
+
+
+
+

We will now plot the precipitation intensity on a Cartopy map and add some spatial features (land borders) using RadarMapDisplay

+
+
+
lon_bnds = [8.2, 9.5]
+lat_bnds = [45.5, 46.5]
+
+display = pyart.graph.RadarMapDisplay(radar)
+
+fig = plt.figure(figsize=(8,8))
+display.plot_ppi_map('radar_estimated_rain_rate', 0, vmin=0, vmax=120.,
+          colorbar_label='', title='Precipitation intensity [mm/h]', gatefilter = gtfilter,
+          min_lon = lon_bnds[0], max_lon = lon_bnds[1],mask_outside = True,
+          min_lat = lat_bnds[0], max_lat = lat_bnds[1],
+          lon_lines=np.arange(lon_bnds[0], lon_bnds[1], .2), resolution='10m',
+          lat_lines=np.arange(lat_bnds[0], lat_bnds[1], .2),
+          lat_0=radar.latitude['data'][0],
+          lon_0=radar.longitude['data'][0], embellish=True)
+
+states_provinces = cartopy.feature.NaturalEarthFeature(
+                category='cultural',
+                name='admin_0_countries',
+                scale='10m',
+                facecolor='none')
+lakes = cartopy.feature.NaturalEarthFeature(
+                category='physical',
+                name='lakes',
+                scale='10m',
+                facecolor='blue')
+rivers = cartopy.feature.NaturalEarthFeature(
+                category='physical',
+                name='rivers',
+                scale='10m',
+                facecolor='blue')
+display.ax.add_feature(states_provinces, edgecolor='gray')
+display.ax.add_feature(lakes, edgecolor='blue', alpha = 0.25)
+display.ax.add_feature(cartopy.feature.RIVERS)
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/pyart/graph/radarmapdisplay.py:281: UserWarning: No projection was defined for the axes. Overridding defined axes and using default axes with projection Lambert Conformal.
+  warnings.warn(
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces_lines.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+
<cartopy.mpl.feature_artist.FeatureArtist at 0x7f57ca2068e0>
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_0_countries.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_lakes.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_rivers_lake_centerlines.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+../../_images/exercice1_swiss_thunderstorm_24_7.png +
+
+

Note that we didn’t estimate precipitation intensity at the ground but only aloft. Within the thunderstorm precipitation intensity is extremely high. This is likely too high because QPE in wet hail is very uncertain. However, even the operational QPE algorithm at MeteoSwiss estimated precipitation intensities at the ground close to 120 mm/h.

+
+
+ + + + +
+ +
+ + +
+

previous

+

Py-ART Gridding

+
+ + +
+

next

+

Processing of Doppler wind data from a Swiss volumetric scan

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/exercice2_swiss_doppler.html b/_preview/104/notebooks/pyart/exercice2_swiss_doppler.html new file mode 100644 index 00000000..e6d1260a --- /dev/null +++ b/_preview/104/notebooks/pyart/exercice2_swiss_doppler.html @@ -0,0 +1,885 @@ + + + + + + + + Processing of Doppler wind data from a Swiss volumetric scan — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
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+ +
+ + + + + + +
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+ +
+ On this page +
+ +
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+ +
+

Processing of Doppler wind data from a Swiss volumetric scan

+

In this exercice we will load low-resolution filtered Swiss C-band data and process it to estimate a profile of horizontal wind aloft the radar. The following topics will be tackled.

+
    +

  • Dealiasing of radial velocity

    • +

  • CAPPI plots and profiles

    • +

  • PseudoRHI profiles

    • +

  • Computation of a VAD (velocity azimuth display)

    • +
+
+
+
# Imports
+
+import numpy as np
+import cartopy.crs as ccrs
+import cartopy
+import matplotlib.pyplot as plt
+import glob
+
+import pyart
+pyart.config.load_config('mch_config.py')
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+

Reading and dealiasing the data

+

The Swiss operational C-band radar network performs 20 PPIs at 20 different elevations (from 0.2° to 40°) every 5 minutes. All PPIs are stored in separate files.

+

We will thus read all 20 elevations one after the other for a given timestep and use the Py-ART function join_radar to merge them all into a single radar object which containes 20 sweeps.

+

To avoid using too much memory we will read the pre-processed MeteoSwiss radar data which has a resolution of 500 m in range.

+

At the end, we will also dealias the radial velocity field. Indeed, at lower elevations, low PRFs are used which result in low Nyquist velocities between 8-12 m/s. This means that a lot of folding will occur, especially in strong winds. In this example we use the simplest dealiasing method of Py-ART which performs by finding regions of similar velocities and unfolding and merging pairs of regions until all regions are unfolded.

+
+
+
# Read all 20 elevations for one timestep
+files_radar = sorted(glob.glob('./data/exercice2_swiss_doppler/MLL221790725*'))
+for i,f in enumerate(files_radar):
+    radar = pyart.io.read_cfradial(f)
+    
+    if i == 0:
+        radar_merged = radar
+    else:
+        radar_merged = pyart.util.join_radar(radar_merged, 
+                                       radar)
+        
+corr_vel = pyart.correct.dealias_region_based(radar_merged)
+radar_merged.add_field('corrected_velocity', corr_vel)
+
+
+
+
+

We will now plot the raw and dealiased velocities at two different elevations to see the effect of the correction.

+
+
+
fig, ax = plt.subplots(2,2, figsize=(10,10), sharex= True, sharey=True)
+ax = ax.ravel()
+display = pyart.graph.RadarDisplay(radar_merged)
+display.plot_ppi('velocity', 2, vmin=-30, vmax=30., ax = ax[0], title='El=1 deg',
+                 colorbar_label = 'Mean Doppler velocity (m/s)')
+display.plot_ppi('corrected_velocity', 2, vmin=-30, vmax=30., title='El=1 deg',
+                      ax = ax[1], colorbar_label = 'corr. Mean Doppler velocity (m/s)')
+display.plot_ppi('velocity', 6, vmin=-30, vmax=30., ax = ax[2], title='El=4.5 deg',
+                 colorbar_label = 'Mean Doppler velocity (m/s)')
+display.plot_ppi('corrected_velocity', 6, vmin=-30, vmax=30., title='El=4.5 deg',
+                      ax = ax[3], colorbar_label = 'corr. Mean Doppler velocity (m/s)')
+ax[0].set_xlim([-50,50])
+ax[0].set_ylim([-50,50])
+for a in ax:
+    a.set_aspect('equal', 'box')
+
+
+
+
+../../_images/exercice2_swiss_doppler_5_0.png +
+
+

Indeed the raw velocity shows alternating bands of negative and positive velocities which indicates aliasing. The dealiased velocity looks much less discontinuous. Note however that a major difficulty for these algorithms is the presence of isolated pixels, which tend to get arbitrary values as can be seen in the south of the radar.

+
+
+

CAPPI plots

+

We will now create a CAPPI (constant altitude PPI) of the reflectivity during this event. The idea is to interpolate the volumetric scan on a 3D Cartesian grid using the function grid_from_radars. Here we will create slices every 500 m from 500 m to 8000 m above the radar.

+
+
+
zmin = 500
+zmax = 8000
+ymin= xmin = -100000
+ymax = xmax = 100000
+lat = float(radar.latitude['data'])
+lon = float(radar.longitude['data'])
+alt = float(radar.altitude['data'])
+# number of grid points in cappi
+cappi_res_h = 500
+cappi_res_v = 500
+ny = int((ymax-ymin)/cappi_res_h)+1
+nx = int((xmax-xmin)/cappi_res_h)+1
+nz = int((zmax-zmin)/cappi_res_v)+1
+
+cappi_zh = pyart.map.grid_from_radars(radar_merged, grid_shape=(nz, ny, nx),
+        grid_limits=((zmin, zmax), (ymin, ymax),
+                     (xmin, xmax)),
+        fields=['reflectivity'])
+
+
+
+
+

Now we plot the reflectivity at 4 different altitudes (0.5, 3, 5.5 and 8 km), as well as a profile along as a W-E profile at the radar location throught the thunderstorm.

+
+
+
display = pyart.graph.GridMapDisplay(cappi_zh)
+projection = ccrs.PlateCarree()
+fig = plt.figure(figsize=(18,14))
+ax = plt.subplot(221, projection = projection)
+display.plot_grid('reflectivity',0, ax = ax, projection = projection)
+ax = plt.subplot(222, projection = projection)
+display.plot_grid('reflectivity',5, ax = ax, projection = projection)
+ax = plt.subplot(223, projection = projection)
+display.plot_grid('reflectivity',10, ax = ax, projection = projection)
+ax = plt.subplot(224, projection = projection)
+display.plot_grid('reflectivity',15, ax = ax, projection = projection)
+
+ax = fig.add_axes([0.25, -0.20, .5, .25])
+display.plot_latitude_slice('reflectivity', lon=lon, lat=lat, ax = ax)
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_1_states_provinces_lines.zip
+  warnings.warn(f'Downloading: {url}', DownloadWarning)
+
+
+../../_images/exercice2_swiss_doppler_10_1.png +
+
+

We will now create a pseudo RHI (altitudinal cross-section through a set of PPIs) of the radial velocity and the reflectivity through the thunderstorm at azimuth 270° (to the west).

+
+
+
pseudorhi = pyart.util.cross_section_ppi(radar_merged, [270])
+
+display = pyart.graph.RadarDisplay(pseudorhi)
+fig, ax = plt.subplots(2,1, sharex=True,sharey=True, figsize= (10,10))
+display.plot_rhi('corrected_velocity', ax = ax[0], vmin = -30, vmax = 30)
+display.plot_rhi('reflectivity', ax = ax[1])
+ax[0].set_ylim([0,20])
+ax[0].set_xlim([0,100])
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/core/fromnumeric.py:758: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
+  a.partition(kth, axis=axis, kind=kind, order=order)
+
+
+
(0.0, 100.0)
+
+
+../../_images/exercice2_swiss_doppler_12_2.png +
+
+

The convention at MeteoSwiss (which is different from the one used by Py-ART) is that positive velocities are moving away from the radar. In this example we see clearly the downdraft in the center of storm and the updraft in its surroundings.

+
+
+

Velocity azimuth display (VAD)

+

We will now make a VAD retrieval to estimate the horizontal wind profile above the radar. This technique requires to have measurements in as many azimuths as possible and works better for stratiform rain when the radar coverage is wider. We will load data from a cold front event on the 13th July 2021 near the Albis radar (south of Zürich), that showed widespread precipitation around the radar.

+

Unfortunately the VAD estimation technique in Py-ART can process only one sweep at a time. So we will average the wind vectors obtained over all sweeps to obtain a more reliable estimate. Note that we skip the first four sweeps which are more prone to ground echoes and have a very low Nyquist velocity.

+
+
+
files_radar = sorted(glob.glob('./data/question_pyart_meteoswiss/MLA211941205*'))
+u_allsweeps = []
+v_allsweeps = [] 
+zlevels = np.arange(100,5000,100)
+speed = []
+for i,f in enumerate(files_radar[4:]):
+    radar = pyart.io.read_cfradial(f)
+    corr_vel = pyart.correct.dealias_region_based(radar)
+    corr_vel['data'] *= -1 
+    radar.add_field('corrected_velocity_neg', corr_vel)
+    
+    vad = pyart.retrieve.vad_browning(radar, 'corrected_velocity_neg', z_want = zlevels)
+    u_allsweeps.append(vad.u_wind)
+    v_allsweeps.append(vad.v_wind)
+    
+u_avg = np.nanmean(np.array(u_allsweeps), axis = 0)
+v_avg = np.nanmean(np.array(v_allsweeps), axis = 0)
+orientation = np.rad2deg(np.arctan2(-u_avg, -v_avg))%360
+speed = np.sqrt(u_avg**2 + v_avg**2)
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/pyart/retrieve/vad.py:340: RuntimeWarning: Mean of empty slice
+  mean_velocity_per_gate = np.nanmean(velocities, axis=0).reshape(1, -1)
+
+
+
max height 9929.0  meters
+min height 10.0  meters
+max height 11404.0  meters
+min height 15.0  meters
+
+
+
max height 12673.0  meters
+min height 19.0  meters
+max height 17022.0  meters
+min height 24.0  meters
+
+
+
max height 16949.0  meters
+min height 28.0  meters
+max height 16599.0  meters
+min height 32.0  meters
+max height 17070.0  meters
+min height 37.0  meters
+
+
+
max height 17028.0  meters
+min height 41.0  meters
+max height 16966.0  meters
+min height 47.0  meters
+max height 17123.0  meters
+min height 56.0  meters
+
+
+
max height 17229.0  meters
+min height 69.0  meters
+max height 17135.0  meters
+min height 85.0  meters
+max height 17301.0  meters
+min height 105.0  meters
+max height 16921.0  meters
+min height 124.0  meters
+
+
+
max height 17097.0  meters
+min height 143.0  meters
+max height 17217.0  meters
+min height 160.0  meters
+
+
+
+
+

Note that because the convention at MeteoSwiss is different than the one in Py-ART we have to flip the sign of the radial velocity field.

+

Finally we do a plot of the vertical profiles or horizontal wind speed and direction

+
+
+
fig,ax = plt.subplots(1,2, sharey=True)
+ax[0].plot(speed*2, zlevels+radar.altitude['data'])
+ax[1].plot(orientation, zlevels+radar.altitude['data'])
+ax[0].set_xlabel('Wind speed [m/s]')
+ax[1].set_xlabel('Wind direction [deg]')
+ax[0].set_ylabel('Altitude [m]')
+fig.suptitle('Wind profile obtained from VAD')
+
+
+
+
+
Text(0.5, 0.98, 'Wind profile obtained from VAD')
+
+
+../../_images/exercice2_swiss_doppler_18_1.png +
+
+

Now let’s compare this wind profile with the one recorded by the nearest radiosounding operated in Payerne (around 130 km west from the radar):

+radiosounding_pay_20210713 +

Though there are some discrepancies the match is not bad, given the distance and the very different ways of measuring wind!

+
+
+ + + + +
+ +
+ + +
+

previous

+

Filtering and retrievals on raw Swiss C-band data

+
+ + +
+

next

+

Exercice

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/pyart-basics.html b/_preview/104/notebooks/pyart/pyart-basics.html new file mode 100644 index 00000000..efcd4e42 --- /dev/null +++ b/_preview/104/notebooks/pyart/pyart-basics.html @@ -0,0 +1,1138 @@ + + + + + + + + Py-ART Basics — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

Py-ART Basics

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. General overview of Py-ART and its functionality

    2. +

  2. Reading data using Py-ART

    3. +

  3. An overview of the pyart.Radar object

    4. +

  4. Create a Plot of our Radar Data

    5. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Intro to Cartopy

Helpful

Basic features

Matplotlib Basics

Helpful

Basic plotting

NumPy Basics

Helpful

Basic arrays

+
    +

  • Time to learn: 45 minutes

    • +
+
+
+
+

Imports

+
+
+
import os
+import warnings
+
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+
+
+import pyart
+from pyart.testing import get_test_data
+
+warnings.filterwarnings('ignore')
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

An Overview of Py-ART

+
+

History of the Py-ART

+
    +

  • Development began to address the needs of ARM with the acquisition of a number of +new scanning cloud and precipitation radar as part of the American Recovery Act.

    • +

  • The project has since expanded to work with a variety of weather radars and a wider user +base including radar researchers and climate modelers.

    • +

  • The software has been released on GitHub as open source software under a BSD license. +Runs on Linux, OS X. It also runs on Windows with more limited functionality.

    • +
+
+
+

What can PyART Do?

+

Py-ART can be used for a variety of tasks from basic plotting to more complex +processing pipelines. Specific uses for Py-ART include:

+
    +

  • Reading radar data in a variety of file formats.

    • +

  • Creating plots and visualization of radar data.

    • +

  • Correcting radar moments while in antenna coordinates, such as:

    +
      +

    • Doppler unfolding/de-aliasing.

      • +

    • Attenuation correction.

      • +

    • Phase processing using a Linear Programming method.

      • +
    +
    • +

  • Mapping data from one or multiple radars onto a Cartesian grid.

    • +

  • Performing retrievals.

    • +

  • Writing radial and Cartesian data to NetCDF files.

    • +
+
+
+
+

Reading in Data Using Py-ART

+
+

Reading data in using pyart.io.read

+

When reading in a radar file, we use the pyart.io.read module.

+

pyart.io.read can read a variety of different radar formats, such as Cf/Radial, LASSEN, and more. +The documentation on what formats can be read by Py-ART can be found here:

+ +

For most file formats listed on the page, using pyart.io.read should suffice since Py-ART has the ability to automatically detect the file format.

+

Let’s check out what arguments arguments pyart.io.read() takes in!

+
+
+
pyart.io.read?
+
+
+
+
+

Let’s use a sample data file from pyart - which is cfradial format.

+

When we read this in, we get a pyart.Radar object!

+
+
+
file = get_test_data('swx_20120520_0641.nc')
+radar = pyart.io.read(file)
+radar
+
+
+
+
+
Downloading file 'swx_20120520_0641.nc' from 'https://adc.arm.gov/pyart/example_data/swx_20120520_0641.nc' to '/home/jovyan/.cache/pyart-datasets'.
+
+
+
<pyart.core.radar.Radar at 0x7f74c49c0d00>
+
+
+
+
+
+
+

Investigate the pyart.Radar object

+

Within this pyart.Radar object object are the actual data fields.

+

This is where data such as reflectivity and velocity are stored.

+

To see what fields are present we can add the fields and keys additions to the variable where the radar object is stored.

+
+
+
radar.fields.keys()
+
+
+
+
+
dict_keys(['corrected_reflectivity_horizontal', 'reflectivity_horizontal', 'recalculated_diff_phase', 'specific_attenuation', 'unf_dp_phase_shift', 'mean_doppler_velocity', 'diff_phase', 'rain_rate_A', 'norm_coherent_power', 'dp_phase_shift', 'diff_reflectivity', 'proc_dp_phase_shift', 'copol_coeff'])
+
+
+
+
+
+

Extract a sample data field

+

The fields are stored in a dictionary, each containing coordinates, units and more. +All can be accessed by just adding the fields addition to the radar object variable.

+

For an individual field, we add a string in brackets after the fields addition to see +the contents of that field.

+

Let’s take a look at 'corrected_reflectivity_horizontal', which is a common field to investigate.

+
+
+
print(radar.fields['corrected_reflectivity_horizontal'])
+
+
+
+
+
{'_FillValue': -9999.0, 'least_significant_digit': 2, 'units': 'dBZ', 'long_name': 'equivalent_reflectivity_factor', 'valid_min': -45.0, 'valid_max': 80.0, 'standard_name': 'equivalent_reflectivity_factor', 'data': masked_array(
+  data=[[-5.671875, 2.28125, -8.1171875, ..., --, -13.4765625, --],
+        [-5.6171875, 1.8984375, -10.0703125, ..., -2.6796875, -1.5390625,
+         --],
+        [-5.0390625, 2.625, -11.484375, ..., -8.984375, --, --],
+        ...,
+        [-5.9375, 1.46875, -12.3203125, ..., --, --, --],
+        [-5.9609375, 1.53125, -12.84375, ..., --, --, --],
+        [-8.7890625, 2.9140625, -12.09375, ..., --, --, --]],
+  mask=[[False, False, False, ...,  True, False,  True],
+        [False, False, False, ..., False, False,  True],
+        [False, False, False, ..., False,  True,  True],
+        ...,
+        [False, False, False, ...,  True,  True,  True],
+        [False, False, False, ...,  True,  True,  True],
+        [False, False, False, ...,  True,  True,  True]],
+  fill_value=-9999.0,
+  dtype=float32)}
+
+
+
+
+

We can go even further in the dictionary and access the actual reflectivity data.

+

We use add 'data' at the end, which will extract the data array (which is a masked numpy array) from the dictionary.

+
+
+
reflectivity = radar.fields['corrected_reflectivity_horizontal']['data']
+print(type(reflectivity), reflectivity)
+
+
+
+
+
<class 'numpy.ma.core.MaskedArray'> [[-5.671875 2.28125 -8.1171875 ... -- -13.4765625 --]
+ [-5.6171875 1.8984375 -10.0703125 ... -2.6796875 -1.5390625 --]
+ [-5.0390625 2.625 -11.484375 ... -8.984375 -- --]
+ ...
+ [-5.9375 1.46875 -12.3203125 ... -- -- --]
+ [-5.9609375 1.53125 -12.84375 ... -- -- --]
+ [-8.7890625 2.9140625 -12.09375 ... -- -- --]]
+
+
+
+
+

Lets’ check the size of this array…

+
+
+
reflectivity.shape
+
+
+
+
+
(8800, 667)
+
+
+
+
+

This reflectivity data array, numpy array, is a two-dimensional array with dimensions:

+
    +

  • Gates (number of samples away from the radar)

    • +

  • Rays (direction around the radar)

    • +
+
+
+
print(radar.nrays, radar.ngates)
+
+
+
+
+
8800 667
+
+
+
+
+

If we wanted to look the 300th ray, at the second gate, we would use something like the following:

+
+
+
print(reflectivity[300, 2])
+
+
+
+
+
-4.8046875
+
+
+
+
+
+
+
+
+

Plotting our Radar Data

+
+

An Overview of Py-ART Plotting Utilities

+

Now that we have loaded the data and inspected it, the next logical thing to do is to visualize the data! Py-ART’s visualization functionality is done through the objects in the pyart.graph module.

+

In Py-ART there are 4 primary visualization classes in pyart.graph:

+ +

Plotting grid data

+ +
+
+

Use the RadarMapDisplay with our data

+

For the this example, we will be using RadarMapDisplay, using Cartopy to deal with geographic coordinates.

+

We start by creating a figure first.

+
+
+
fig = plt.figure(figsize=[10, 10])
+
+
+
+
+
<Figure size 720x720 with 0 Axes>
+
+
+
+
+

Once we have a figure, let’s add our RadarMapDisplay

+
+
+
fig = plt.figure(figsize=[10, 10])
+display = pyart.graph.RadarMapDisplay(radar)
+
+
+
+
+
<Figure size 720x720 with 0 Axes>
+
+
+
+
+

Adding our map display without specifying a field to plot won’t do anything we need to specifically add a field to field using .plot_ppi_map()

+
+
+
display.plot_ppi_map('corrected_reflectivity_horizontal')
+
+
+
+
+../../_images/pyart-basics_33_0.png +
+
+

By default, it will plot the elevation scan, the the default colormap from Matplotlib… let’s customize!

+

We add the following arguements:

+
    +

  • sweep=3 - The fourth elevation scan (since we are using Python indexing)

    • +

  • vmin=-20 - Minimum value for our plotted field/colorbar

    • +

  • vmax=60 - Maximum value for our plotted field/colorbar

    • +

  • projection=ccrs.PlateCarree() - Cartopy latitude/longitude coordinate system

    • +

  • cmap='pyart_HomeyerRainbow' - Colormap to use, selecting one provided by PyART

    • +
+
+
+
fig = plt.figure(figsize=[12, 12])
+display = pyart.graph.RadarMapDisplay(radar)
+display.plot_ppi_map('corrected_reflectivity_horizontal',
+                     sweep=3,
+                     vmin=-20,
+                     vmax=60,
+                     projection=ccrs.PlateCarree(),
+                     cmap='pyart_HomeyerRainbow')
+plt.show()
+
+
+
+
+../../_images/pyart-basics_35_0.png +
+
+

You can change many parameters in the graph by changing the arguments to plot_ppi_map. As you can recall from earlier. simply view these arguments in a Jupyter notebook by typing:

+
+
+
display.plot_ppi_map?
+
+
+
+
+

For example, let’s change the colormap to something different

+
+
+
fig = plt.figure(figsize=[12, 12])
+display = pyart.graph.RadarMapDisplay(radar)
+display.plot_ppi_map('corrected_reflectivity_horizontal',
+                     sweep=3,
+                     vmin=-20,
+                     vmax=60,
+                     projection=ccrs.PlateCarree(),
+                     cmap='pyart_Carbone42')
+plt.show()
+
+
+
+
+../../_images/pyart-basics_39_0.png +
+
+

Or, let’s view a different elevation scan! To do this, change the sweep parameter in the plot_ppi_map function.

+
+
+
fig = plt.figure(figsize=[12, 12])
+display = pyart.graph.RadarMapDisplay(radar)
+display.plot_ppi_map('corrected_reflectivity_horizontal',
+                     sweep=0,
+                     vmin=-20,
+                     vmax=60,
+                     projection=ccrs.PlateCarree(),
+                     cmap='pyart_Carbone42')
+plt.show()
+
+
+
+
+../../_images/pyart-basics_41_0.png +
+
+

Let’s take a look at a different field - for example, correlation coefficient (corr_coeff)

+
+
+
fig = plt.figure(figsize=[12, 12])
+display = pyart.graph.RadarMapDisplay(radar)
+display.plot_ppi_map('copol_coeff',
+                     sweep=0,
+                     vmin=0.8,
+                     vmax=1.,
+                     projection=ccrs.PlateCarree(),
+                     cmap='pyart_Carbone42')
+plt.show()
+
+
+
+
+../../_images/pyart-basics_43_0.png +
+
+
+
+
+
+

Summary

+

Within this notebook, we covered the basics of working with radar data using pyart, including:

+
    +

  • Reading in a file using pyart.io

    • +

  • Investigating the Radar object

    • +

  • Visualizing radar data using the RadarMapDisplay

    • +
+
+

What’s Next

+

In the next few notebooks, we walk through gridding radar data, applying data cleaning methods, and advanced visualization methods!

+
+
+
+

Resources and References

+

Py-ART essentials links:

+
    +

  • Landing page

    • +

  • Examples

    • +

  • Source Code

    • +

  • Mailing list

    • +

  • Issue Tracker

    • +
+
+
+ + + + +
+ +
+ + +
+

previous

+

Py-ART Tutorial

+
+ + +
+

next

+

Py-ART Gridding

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/pyart-gridding.html b/_preview/104/notebooks/pyart/pyart-gridding.html new file mode 100644 index 00000000..b5d777da --- /dev/null +++ b/_preview/104/notebooks/pyart/pyart-gridding.html @@ -0,0 +1,2033 @@ + + + + + + + + Py-ART Gridding — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

Py-ART Gridding

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. What is gridding and why is it important?

    2. +

  2. An overview of gridding with Py-ART

    3. +

  3. How to choose a gridding routine

    4. +

  4. Gridding multiple radars to the same grid

    5. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Py-ART Basics

Helpful

Basic features

Intro to Cartopy

Helpful

Basic features

Matplotlib Basics

Helpful

Basic plotting

NumPy Basics

Helpful

Basic arrays

+
    +

  • Time to learn: 45 minutes

    • +
+
+
+
+

Imports

+
+
+
import os
+import warnings
+
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+
+
+import pyart
+from pyart.testing import get_test_data
+
+warnings.filterwarnings('ignore')
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

What is gridding and why is it important?

+
+

Antenna vs. Cartesian Coordinates

+

Radar data, by default, is stored in a polar (or antenna) coordinate system, with the data coordinates stored as an angle (ranging from 0 to 360 degrees with 0 == North), and a radius from the radar, and an elevation which is the angle between the ground and the ground.

+

This format can be challenging to plot, since it is scan/radar specific. Also, it can make comparing with model data, which is on a lat/lon grid, challenging since one would need to transform the model daa cartesian coordinates to polar/antenna coordiantes.

+

Fortunately, PyART has a variety of gridding routines, which can be used to grid your data to a Cartesian grid. Once it is in this new grid, one can easily slice/dice the dataset, and compare to other data sources.

+
+
+

Why is Gridding Important?

+

Gridding is essential to combining multiple data sources (ex. multiple radars), and comparing to other data sources (ex. model data). There are also decisions that are made during the gridding process that have a large impact on the regridded data - for example:

+
    +

  • What resolution should my grid be?

    • +

  • Which interpolation routine should I use?

    • +

  • How smooth should my interpolated data be?

    • +
+

While there is not always a right or wrong answer, it is important to understand the options available, and document which routine you used with your data! Also - experiment with different options and choose the best for your use case!

+
+
+
+

An overview of gridding with Py-ART

+

Let’s dig into the regridding process with PyART!

+
+

Read in and Visualize a Test Dataset

+

Let’s start with the same file used in the previous notebook (PyART Basics), which is a radar file from Northern Oklahoma.

+
+
+
file = get_test_data('swx_20120520_0641.nc')
+radar = pyart.io.read(file)
+
+
+
+
+

Let’s plot up quick look of reflectivity, at the lowest elevation scan (closest to the ground)

+
+
+
fig = plt.figure(figsize=[12, 12])
+display = pyart.graph.RadarDisplay(radar)
+display.plot_ppi('corrected_reflectivity_horizontal',
+                 cmap='pyart_HomeyerRainbow')
+
+
+
+
+../../_images/pyart-gridding_11_0.png +
+
+

As mentioned before, the dataset is currently in the antenna coordinate system measured as distance from the radar

+
+
+

Setup our Gridding Routine with pyart.map.grid_from_radars()

+

Py-ART has the Grid object which has characteristics similar to that of the Radar object, except that the data are stored in Cartesian coordinates instead of the radar’s antenna coordinates.

+
+
+
pyart.core.Grid?
+
+
+
+
+

We can transform our data into this grid object, from the radars, using pyart.map.grid_from_radars().

+

Beforing gridding our data, we need to make a decision about the desired grid resolution and extent. For example, one might imagine a grid configuration of:

+
    +

  • Grid extent/limits

    +
      +

    • 20 km in the x-direction (north/south)

      • +

    • 20 km in the y-direction (west/east)

      • +

    • 15 km in the z-direction (vertical)

      • +
    +
    • +

  • 500 m spatial resolution

    • +
+

The pyart.map.grid_from_radars() function takes the grid shape and grid limits as input, with the order (z, y, x).

+

Let’s setup our configuration, setting our grid extent first, with the distance measured in meters

+
+
+
z_grid_limits = (500.,15_000.)
+y_grid_limits = (-20_000.,20_000.)
+x_grid_limits = (-20_000.,20_000.)
+
+
+
+
+

Now that we have our grid limits, we can set our desired resolution (again, in meters)

+
+
+
grid_resolution = 500
+
+
+
+
+

Let’s compute our grid shape - using the extent and resolution to compute the number of grid points in each direction.

+
+
+
def compute_number_of_points(extent, resolution):
+    return int((extent[1] - extent[0])/resolution)
+
+
+
+
+

Now that we have a helper function to compute this, let’s apply it to our vertical dimension

+
+
+
z_grid_points = compute_number_of_points(z_grid_limits, grid_resolution)
+z_grid_points
+
+
+
+
+
29
+
+
+
+
+

We can apply this to the horizontal (x, y) dimensions as well.

+
+
+
x_grid_points = compute_number_of_points(x_grid_limits, grid_resolution)
+y_grid_points = compute_number_of_points(y_grid_limits, grid_resolution)
+
+print(z_grid_points,
+      y_grid_points,
+      x_grid_points)
+
+
+
+
+
29 80 80
+
+
+
+
+
+

Use our configuration to grid the data!

+

Now that we have the grid shape and grid limits, let’s grid up our radar!

+
+
+
grid = pyart.map.grid_from_radars(radar,
+                                  grid_shape=(z_grid_points,
+                                              y_grid_points,
+                                              x_grid_points),
+                                  grid_limits=(z_grid_limits,
+                                               y_grid_limits,
+                                               x_grid_limits),
+                                 )
+grid
+
+
+
+
+
<pyart.core.grid.Grid at 0x7fd0ef4a5fa0>
+
+
+
+
+

We now have a pyart.core.Grid object!

+
+
+
+

Plot up the Grid Object

+
+

Plot a horizontal view of the data

+

We can use the GridMapDisplay from pyart.graph to visualize our regridded data, starting with a horizontal view (slice along a single vertical level)

+
+
+
display = pyart.graph.GridMapDisplay(grid)
+display.plot_grid('corrected_reflectivity_horizontal',
+                  level=0,
+                  vmin=-20,
+                  vmax=60,
+                  cmap='pyart_HomeyerRainbow')
+
+
+
+
+../../_images/pyart-gridding_29_0.png +
+
+
+
+

Plot a Latitudinal Slice

+

We can also slice through a single latitude or longitude!

+
+
+
display.plot_latitude_slice('corrected_reflectivity_horizontal',
+                            lat=36.5,
+                            vmin=-20,
+                            vmax=60,
+                            cmap='pyart_HomeyerRainbow')
+plt.xlim([-20, 20]);
+
+
+
+
+../../_images/pyart-gridding_32_0.png +
+
+
+
+

Plot with Xarray

+

Another neat feature of the Grid object is that we can transform it to an xarray.Dataset!

+
+
+
ds = grid.to_xarray()
+ds
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:                            (time: 1, z: 29, y: 80, x: 80)
+Coordinates:
+  * time                               (time) object 2011-05-20 06:42:11
+  * z                                  (z) float64 500.0 1.018e+03 ... 1.5e+04
+    lat                                (y) float64 36.31 36.32 ... 36.67 36.67
+    lon                                (x) float64 -97.82 -97.81 ... -97.37
+  * y                                  (y) float64 -2e+04 -1.949e+04 ... 2e+04
+  * x                                  (x) float64 -2e+04 -1.949e+04 ... 2e+04
+Data variables: (12/14)
+    rain_rate_A                        (time, z, y, x) float32 0.7812 ... 0.0
+    mean_doppler_velocity              (time, z, y, x) float32 -11.43 ... 1.22
+    norm_coherent_power                (time, z, y, x) float32 0.2725 ... 0.0...
+    unf_dp_phase_shift                 (time, z, y, x) float32 66.88 ... 42.07
+    recalculated_diff_phase            (time, z, y, x) float32 0.3767 ... 0.0...
+    proc_dp_phase_shift                (time, z, y, x) float32 67.05 ... 42.07
+    ...                                 ...
+    dp_phase_shift                     (time, z, y, x) float32 153.0 ... nan
+    diff_reflectivity                  (time, z, y, x) float32 0.0 0.0 ... 0.0
+    corrected_reflectivity_horizontal  (time, z, y, x) float32 11.35 ... nan
+    specific_attenuation               (time, z, y, x) float32 0.007137 ... 0.0
+    reflectivity_horizontal            (time, z, y, x) float32 -3.618 ... nan
+    ROI                                (time, z, y, x) float32 765.6 ... 1.49...
+
+

Now, our plotting routine is a one-liner, starting with the horizontal slice:

+
+
+
ds.isel(z=0).corrected_reflectivity_horizontal.plot(cmap='pyart_HomeyerRainbow',
+                                                    vmin=-20,
+                                                    vmax=60);
+
+
+
+
+../../_images/pyart-gridding_37_0.png +
+
+

And a vertical slice at a given y dimension (latitude)

+
+
+
ds.sel(y=1300,
+       method='nearest').corrected_reflectivity_horizontal.plot(cmap='pyart_HomeyerRainbow',
+                                                                vmin=-20,
+                                                                vmax=60);
+
+
+
+
+../../_images/pyart-gridding_39_0.png +
+
+
+
+
+
+
+

Summary

+

Within this notebook, we covered the basics of gridding radar data using pyart, including:

+
    +

  • What we mean by gridding and why is it matters

    • +

  • Configuring your gridding routine

    • +

  • Visualize gridded radar data

    • +
+
+

What’s Next

+

In the next few notebooks, we walk through applying data cleaning methods, and advanced visualization methods!

+
+
+
+

Resources and References

+

Py-ART essentials links:

+
    +

  • Landing page

    • +

  • Examples

    • +

  • Source Code

    • +

  • Mailing list

    • +

  • Issue Tracker

    • +
+
+
+ + + + +
+ +
+ + +
+

previous

+

Py-ART Basics

+
+ + +
+

next

+

Filtering and retrievals on raw Swiss C-band data

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart/question_pyart_meteoswiss.html b/_preview/104/notebooks/pyart/question_pyart_meteoswiss.html new file mode 100644 index 00000000..ca323088 --- /dev/null +++ b/_preview/104/notebooks/pyart/question_pyart_meteoswiss.html @@ -0,0 +1,578 @@ + + + + + + + + Exercice — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

Exercice

+

The goal of this exercice is to do a QPE for every sweep and perform a weighted average of precipitation aloft to get an estimation of the precipitation intensity at the ground. The following steps will need to be taken:

+
    +

  1. Load all radar files in /data/question_pyart_meteoswiss and merge them into one single radar object

    2. +

  2. Perform attenuation correction of ZH, you can use a constant freezing level height of 2700 m.

    3. +

  3. Estimate the QPE with a a polynomial Z-R relation.

    4. +

  4. Compute a CAPPI of the resulting radar estimate rain rate from 500 to 8000 m above the radar using a vertical resolution of 100 m and a horizontal resolution of 500 m.

    5. +

  5. Using numpy, perform a weighted average of all CAPPI levels using the weights

    6. +
+
$w(z) = exp(-0.5\cdot z)$, where $z$ is the height above the radar in km.
+

Finally display the resulting QPE on its latitude/longitude grid using pcolormesh (matplotlib).

+

The solution can be found here

+
+ + + + +
+ +
+ + +
+

previous

+

Processing of Doppler wind data from a Swiss volumetric scan

+
+ + +
+

next

+

Exercice Sample Solution

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.html b/_preview/104/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.html new file mode 100644 index 00000000..5d2a52b2 --- /dev/null +++ b/_preview/104/notebooks/pyart2baltrad/baltrad_pyart_rain_rate_example.html @@ -0,0 +1,940 @@ + + + + + + + + In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART. — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART.

+
+

This is a severe flooding case from July 8, 2013 in Toronto, Canada, with radar data from the King City, Ontario, radar.

+
+
+
%matplotlib inline
+
+
+
+
+

Import the necessary modules.

+
+
+
import numpy as np
+import pyart
+import baltrad_pyart_bridge as bridge  # routines to pass data from Py-ART and BALTRAD
+import _raveio   # BALTRAD's input/output module
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+

Read in the data using RAVE (a component of BALTRAD)

+
+
+
+

Rain rate retrieval using specific attenuation using BALTRAD and Py-ART

+
+
+
rio = _raveio.open('data/WKR_201307082030.h5')
+
+
+
+
+

Convert the data to a Py-ART Radar object.

+
+
+
radar = bridge.raveio2radar(rio)
+
+
+
+
+

Examine some of the radar moments.

+
+
+
display = pyart.graph.RadarDisplay(radar)
+
+
+
+
+
+
+
display.plot_ppi('DBZH', 0, vmin=-15, vmax=60)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_11_0.png +
+
+
+
+
display.plot_ppi('PHIDP', 0, vmin=0, vmax=180)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_12_0.png +
+
+
+
+
display.plot_ppi('RHOHV', 0, vmin=0, vmax=1.0, mask_outside=False)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_13_0.png +
+
+
+
+
display.plot_ppi('SQIH', 0, vmin=0, vmax=1, mask_outside=False)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_14_0.png +
+
+

Calculate the specific attenuation and attenuation corrected reflectivity using Py-ART, add these field to the radar object.

+
+
+
spec_at, cor_z = pyart.correct.calculate_attenuation(
+    radar, 0, doc=0, refl_field='DBZH', ncp_field='SQIH', 
+    rhv_field='RHOHV', phidp_field='PHIDP', 
+    fzl=8000,)
+# use the parameter below for a more 'cleanup up' attenuation field
+#ncp_min=-1, rhv_min=-1)
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/core/fromnumeric.py:758: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
+  a.partition(kth, axis=axis, kind=kind, order=order)
+
+
+
+
+
+
+
radar.add_field('specific_attenuation', spec_at)
+radar.add_field('corrected_reflectivity', cor_z)
+
+
+
+
+

Examine these two new fields.

+
+
+
display.plot_ppi('specific_attenuation', 0, vmin=0, vmax=0.1)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_19_0.png +
+
+
+
+
display.plot_ppi('corrected_reflectivity', 0, vmin=-15, vmax=60)
+display.plot_range_rings([50, 100, 150])
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_20_0.png +
+
+

Calculate the rain rate from the specific attenuation using a power law determined from the ARM Southern Great Plains site. Mask values where the attenuation is not valid (when the cross correlation ratio or signal quality is low). Add this field to the radar object.

+
+
+
R = 300.0 * (radar.fields['specific_attenuation']['data']) ** 0.89
+rain_rate_dic = pyart.config.get_metadata('rain_rate')
+rain_rate_dic['units'] = 'mm/hr'
+rate_not_valid = np.logical_or(
+    (radar.fields['SQIH']['data'] < 0.4),
+    (radar.fields['RHOHV']['data'] < 0.8))
+rain_rate_dic['data'] = np.ma.masked_where(rate_not_valid, R)
+# fill the missing values with 0 for a nicer plot
+rain_rate_dic['data'] = np.ma.filled(rain_rate_dic['data'], 0)
+
+
+
+
+
+
+
radar.add_field('RATE', rain_rate_dic)
+
+
+
+
+

Examine the rain rate

+
+
+
display.plot_ppi('RATE', 0, vmin=0, vmax=50.0)
+
+
+
+
+../../_images/baltrad_pyart_rain_rate_example_25_0.png +
+
+

Create a new RaveIO object from the Py-ART radar object and write this out using Rave

+
+
+
rio_out = bridge.radar2raveio(radar)
+
+
+
+
+
+
+
container = _raveio.new()
+container.object = rio_out.object
+container.save("data/WKR_201307082030_with_rain_rate.h5")
+
+import os
+print("ODIM_H5 file is %i bytes large" % os.path.getsize("data/WKR_201307082030_with_rain_rate.h5"))
+
+
+
+
+
ODIM_H5 file is 6259795 bytes large
+
+
+
+
+
+

Publish a time series of Cartesian products of corrected reflectivity to BALTRAD’s GoogleMapsPlugin

+
+

Using your Browser, preferably anything except Microsoft Internet Explorer, view a pre-loaded product: http://localhost:8080 Use the small Calendar icon in the control panel to select 2013-07-08 20:30. The dropdown box under the date/time field should read “King City, ON”.

+
+
+

Fire up the RAVE Product Generation Framework’s server. This is normally done on the command line.

+
+
+
import os
+os.system("rave_pgf start")
+
+
+
+
+
0
+
+
+
+
+
+
+

Connect to this XML-RPC server and feed it file strings of pre-generated products

+
+
+
import glob, xmlrpc.client
+
+ipath = "/home/vagrant/pyart2baltrad/data/cawkr"
+opath = "/home/vagrant/miniconda/envs/openradar/rave_gmap/web/data/cawkr_gmaps"
+
+server = xmlrpc.client.ServerProxy("http://localhost:8085/RAVE")
+
+fstrs = glob.glob(ipath + "/*.h5")
+
+for ifstr in fstrs:
+    # Output file name must only be date/time string with format: YYYYMMDDHHmm.png
+    dt = os.path.split(ifstr)[1].split('_')[2].split('.')[0]
+    
+    ofstr = opath + "/%s/%s/%s/%s.png" % (dt[:4], dt[4:6], dt[6:8], dt)
+    response = server.generate("se.smhi.rave.creategmapimage", [ifstr], ["outfile",ofstr])
+
+print("Generated %i PNG images for Google Maps" % len(fstrs))
+
+
+
+
+
Generated 0 PNG images for Google Maps
+
+
+
+
+
+
+

Go back to your browser and load a sequence of images. Stop the PGF server.

+
+
+
os.system("rave_pgf stop")
+
+
+
+
+
Waiting for server to shut down ..
+
+
+
...
+
+
+
...
+
+
+
...
+
+
+
...
+
+
+
...
+
+
+
...
+
+
+
...
+
+
+
..
+
+
+
...
+
+
+
...
+
+
+
0
+
+
+
+
+
+
+
+ + + + +
+ +
+ + +
+

previous

+

Interaction of BALTRAD and wradlib via ODIM_H5

+
+ + +
+

next

+

Doppler Velocity Dealiasing with Py-ART and BALTRAD

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.html b/_preview/104/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.html new file mode 100644 index 00000000..10170970 --- /dev/null +++ b/_preview/104/notebooks/pyart2baltrad/pyart_baltrad_dealias_example.html @@ -0,0 +1,674 @@ + + + + + + + + Doppler Velocity Dealiasing with Py-ART and BALTRAD — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

Doppler Velocity Dealiasing with Py-ART and BALTRAD

+

In this notebook Doppler Velocity data from the ARM C-band SAPR radar is read using Py-ART and dealiased using BALTRAD.

+
+
+
%matplotlib inline
+
+
+
+
+

Import the necessary modules

+
+
+
import pyart
+import baltrad_pyart_bridge as bridge  # routines to pass data from Py-ART to BALTRAD
+import _dealias # BALTRAD's dealiasing module
+
+
+
+
+
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
+## library for working with weather radar data. Py-ART is partly
+## supported by the U.S. Department of Energy as part of the Atmospheric
+## Radiation Measurement (ARM) Climate Research Facility, an Office of
+## Science user facility.
+##
+## If you use this software to prepare a publication, please cite:
+##
+##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+

Read in the data using Py-ART

+
+
+
radar = pyart.io.read('data/sgpcsaprppi_20110520095101.nc')
+
+
+
+
+

Examine the velocity data using Py-ART Display object.

+
+
+
display = pyart.graph.RadarDisplay(radar)
+nyquist_velocity = radar.instrument_parameters['nyquist_velocity']['data'][0]
+display.plot_ppi('velocity', 1, colorbar_label='m/s', 
+                 vmin=-nyquist_velocity, vmax=nyquist_velocity)
+
+
+
+
+../../_images/pyart_baltrad_dealias_example_8_0.png +
+
+

Convert the radar data into a RaveIO object with the velocity data having the correct name.

+
+
+
vel_data = radar.fields['velocity']['data']
+radar.add_field_like('velocity', 'VRAD', vel_data)
+rio = bridge.radar2raveio(radar)
+
+
+
+
+

Perform Doppler velocity dealiasing using BALTRAD.

+
+
+
ret = _dealias.dealias(rio.object)
+print("This first scan is dealiased:"), _dealias.dealiased(rio.object.getScan(0))
+
+
+
+
+
This first scan is dealiased:
+
+
+
(None, False)
+
+
+
+
+

Add the dealiased velocity field to the origin Py-ART radar object.

+
+
+
temp = bridge.raveio2radar(rio)
+if 'dealiased_velocity' in radar.fields:
+    radar.fields.pop('dealiased_velocity')
+radar.add_field_like('velocity', 'dealiased_velocity', temp.fields['VRAD']['data'])
+
+
+
+
+

Plot the dealiased velocities.

+
+
+
display.plot_ppi('dealiased_velocity', 1, colorbar_label='m/s', 
+                 vmin=-2*nyquist_velocity, vmax=2*nyquist_velocity)
+
+
+
+
+../../_images/pyart_baltrad_dealias_example_16_0.png +
+
+
+ + + + +
+ +
+ + +
+

previous

+

In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART.

+
+ + +
+

next

+

Developing Open Source Software Packages

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/wradlib/wradlib_data_quality.html b/_preview/104/notebooks/wradlib/wradlib_data_quality.html new file mode 100644 index 00000000..6dfb04b8 --- /dev/null +++ b/_preview/104/notebooks/wradlib/wradlib_data_quality.html @@ -0,0 +1,4902 @@ + + + + + + + + wradlib data quality — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

wradlib logo png

+
+

wradlib data quality

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. Reading radar volume data into xarray based RadarVolume

    2. +

  2. Wrapping numpy-based functions to work with Xarray

    3. +

  3. Clutter detection

    4. +

  4. Beam Blockage calculation

    5. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Xarray Basics

Helpful

Basic Dataset/DataArray

Matplotlib Basics

Helpful

Basic Plotting

Intro to Cartopy

Helpful

Projections

+
    +

  • Time to learn: 10 minutes

    • +
+
+
+
+

Imports

+
+
+
import glob
+import os
+
+import cartopy
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+
+import wradlib as wrl
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

Import Swiss Radar Data from CfRadial1 Volumes

+

We use some of the pyrad example data here. Sweeps are provided as single files, so we open each file separately and create the RadarVolume from the open Datasets.

+
+
+
fglob = "../pyart/data/example_pyrad/22179/MLL22179/MLL2217907250U*.nc"
+flist = glob.glob(fglob)
+flist.sort()
+print("Files available: {}".format(len(flist)))
+
+
+
+
+
Files available: 20
+
+
+
+
+
+
+
ds = [xr.open_dataset(f, group="sweep_1", engine="cfradial1", chunks={}) for f in flist]
+
+
+
+
+
+
+
vol = wrl.io.RadarVolume(engine="cfradial1")
+vol.extend(ds)
+vol.sort(key=lambda x: x.time.min())
+
+
+
+
+
+
+
display(vol)
+
+
+
+
+
<wradlib.RadarVolume>
+Dimension(s): (sweep: 20)
+Elevation(s): (2.5, 4.5, 6.5, -0.2, 1.0, 7.5, 8.5, 11.0, 16.0, 25.0, 35.0, 0.4, 1.6, 3.5, 5.5, 9.5, 13.0, 20.0, 30.0, 40.0)
+
+
+
+
+
+
+
vol.root
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:              (sweep: 20)
+Coordinates:
+    sweep_mode           <U20 'azimuth_surveillance'
+    longitude            float32 8.833
+    altitude             float32 1.626e+03
+    time                 datetime64[ns] 2022-06-28T07:19:28
+    latitude             float32 46.04
+Dimensions without coordinates: sweep
+Data variables:
+    volume_number        int64 0
+    platform_type        <U5 'fixed'
+    instrument_type      <U5 'radar'
+    primary_axis         <U6 'axis_z'
+    time_coverage_start  <U20 '2022-06-28T07:19:28Z'
+    time_coverage_end    <U20 '2022-06-28T07:23:44Z'
+    sweep_group_name     (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_19'
+    sweep_fixed_angle    (sweep) float32 2.5 4.5 6.5 -0.2 ... 20.0 30.0 40.0
+Attributes:
+    version:          None
+    title:            None
+    institution:      None
+    references:       None
+    source:           None
+    history:          None
+    comment:          im/exported using wradlib
+    instrument_name:  None
+    fixed_angle:      2.5
+
+
+
+
sweep_number = 3
+display(vol[sweep_number])
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:                              (azimuth: 360, range: 492, frequency: 1)
+Coordinates:
+    rtime                                (azimuth) datetime64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+  * range                                (range) float32 250.0 ... 2.457e+05
+  * azimuth                              (azimuth) float32 0.5246 ... 359.5
+    elevation                            (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    sweep_mode                           <U20 ...
+  * frequency                            (frequency) float32 5.451e+09
+    latitude                             float32 ...
+    longitude                            float32 ...
+    altitude                             float32 ...
+    time                                 datetime64[ns] ...
+Data variables: (12/20)
+    reflectivity                         (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    signal_to_noise_ratio                (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    reflectivity_vv                      (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    differential_reflectivity            (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    uncorrected_cross_correlation_ratio  (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    uncorrected_differential_phase       (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    ...                                   ...
+    radar_beam_width_v                   float32 ...
+    pulse_width                          (azimuth) timedelta64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+    nyquist_velocity                     (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    time_coverage_start                  |S32 ...
+    time_coverage_end                    |S32 ...
+    volume_number                        int32 ...
+Attributes:
+    fixed_angle:  -0.2
+
+
+
+

Clutter detection with Gabella

+

While in Switzerland, why not use the well-known clutter detection scheme by Marco Gabella et. al.

+
+

Wrap Gabella Clutter detection in Xarray apply_ufunc

+

The routine is implemented in wradlib in pure Numpy. Numpy based processing routines can be transformed to a first class Xarray citizen with the help of xr.apply_ufunc.

+
+
+
def extract_clutter(da, wsize=5, thrsnorain=0, tr1=6.0, n_p=6, tr2=1.3, rm_nans=False):
+    return xr.apply_ufunc(
+        wrl.clutter.filter_gabella,
+        da,
+        input_core_dims=[["azimuth", "range"]],
+        output_core_dims=[["azimuth", "range"]],
+        dask="parallelized",
+        kwargs=dict(
+            wsize=wsize,
+            thrsnorain=thrsnorain,
+            tr1=tr1,
+            n_p=n_p,
+            tr2=tr2,
+            rm_nans=rm_nans,
+        ),
+    )
+
+
+
+
+
+
+

Calculate clutter map

+

Now we apply Gabella scheme and add the result to the Dataset.

+
+
+
swp = vol[sweep_number]
+clmap = swp.reflectivity_hh_clut.pipe(
+    extract_clutter, wsize=5, thrsnorain=0.0, tr1=21.0, n_p=23, tr2=1.3, rm_nans=False
+)
+swp = swp.assign({"CMAP": clmap})
+display(swp)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:                              (azimuth: 360, range: 492, frequency: 1)
+Coordinates:
+    rtime                                (azimuth) datetime64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+  * range                                (range) float32 250.0 ... 2.457e+05
+  * azimuth                              (azimuth) float32 0.5246 ... 359.5
+    elevation                            (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    sweep_mode                           <U20 ...
+  * frequency                            (frequency) float32 5.451e+09
+    latitude                             float32 ...
+    longitude                            float32 ...
+    altitude                             float32 ...
+    time                                 datetime64[ns] ...
+Data variables: (12/21)
+    reflectivity                         (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    signal_to_noise_ratio                (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    reflectivity_vv                      (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    differential_reflectivity            (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    uncorrected_cross_correlation_ratio  (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    uncorrected_differential_phase       (azimuth, range) float32 dask.array<chunksize=(360, 492), meta=np.ndarray>
+    ...                                   ...
+    pulse_width                          (azimuth) timedelta64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+    nyquist_velocity                     (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    time_coverage_start                  |S32 ...
+    time_coverage_end                    |S32 ...
+    volume_number                        int32 ...
+    CMAP                                 (azimuth, range) bool dask.array<chunksize=(360, 492), meta=np.ndarray>
+Attributes:
+    fixed_angle:  -0.2
+
+
+
+

Plot Reflectivities, Clutter and Cluttermap

+
+
+
from osgeo import osr
+
+wgs84 = osr.SpatialReference()
+wgs84.ImportFromEPSG(4326)
+
+
+
+
+
0
+
+
+
+
+
+
+
fig = plt.figure(figsize=(15, 12))
+
+swpx = swp.sel(range=slice(0, 100000)).pipe(wrl.georef.georeference_dataset, proj=wgs84)
+
+ax1 = fig.add_subplot(221)
+swpx.reflectivity_hh_clut.plot(x="x", y="y", ax=ax1, vmin=0, vmax=60)
+ax1.set_title("Reflectivity raw")
+
+ax2 = fig.add_subplot(222)
+swpx.CMAP.plot(x="x", y="y", ax=ax2)
+ax2.set_title("Cluttermap")
+
+ax3 = fig.add_subplot(223)
+swpx.reflectivity_hh_clut.where(swpx.CMAP == 1).plot(
+    x="x", y="y", ax=ax3, vmin=0, vmax=60
+)
+ax3.set_title("Clutter")
+
+ax4 = fig.add_subplot(224)
+swpx.reflectivity_hh_clut.where(swpx.CMAP < 1).plot(
+    x="x", y="y", ax=ax4, vmin=0, vmax=60
+)
+ax4.set_title("Reflectivity clutter removed")
+
+
+
+
+
Text(0.5, 1.0, 'Reflectivity clutter removed')
+
+
+../../_images/wradlib_data_quality_21_1.png +
+
+
+
+
+

SRTM based clutter and beamblockage processing

+
+

Download needed SRTM data

+

For the course we already provide the needed SRTM tiles. For normal operation you would need a NASA EARTHDATA account and a connected bearer token.

+

The data will be loaded using GDAL machinery and transformed into an Xarray DataArray.

+
+
+
extent = wrl.zonalstats.get_bbox(swpx.x.values, swpx.y.values)
+extent
+
+
+
+
+
{'left': 7.544804108214879,
+ 'right': 10.121619417254358,
+ 'bottom': 45.14348398510345,
+ 'top': 46.9378914870384}
+
+
+
+
+
+
+
# apply fake token, data is already available
+os.environ["WRADLIB_EARTHDATA_BEARER_TOKEN"] = ""
+# set location of wradlib-data, where wradlib will search for any available data
+os.environ["WRADLIB_DATA"] = "data/wradlib-data"
+# get the tiles
+dem = wrl.io.get_srtm(extent.values())
+
+
+
+
+
+
+
elevation = wrl.georef.read_gdal_values(dem)
+coords = wrl.georef.read_gdal_coordinates(dem)
+
+
+
+
+
+
+
elev = xr.DataArray(
+    data=elevation,
+    dims=["y", "x"],
+    coords={"lat": (["y", "x"], coords[..., 1]), "lon": (["y", "x"], coords[..., 0])},
+)
+elev
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (y: 2401, x: 4801)>
+array([[ 422,  422,  422, ..., 1846, 1798, 1745],
+       [ 422,  422,  422, ..., 1856, 1807, 1748],
+       [ 422,  422,  422, ..., 1815, 1781, 1740],
+       ...,
+       [2403, 2379, 2357, ...,   14,   15,   15],
+       [2408, 2387, 2365, ...,   14,   15,   14],
+       [2428, 2411, 2397, ...,   13,   13,   13]], dtype=int16)
+Coordinates:
+    lat      (y, x) float64 47.0 47.0 47.0 47.0 47.0 ... 45.0 45.0 45.0 45.0
+    lon      (y, x) float64 7.0 7.001 7.002 7.003 7.003 ... 11.0 11.0 11.0 11.0
+Dimensions without coordinates: y, x
+
+
+
+

Plot Clutter on DEM

+
+
+
fig = plt.figure(figsize=(13, 10))
+ax1 = fig.add_subplot(111)
+
+swpx.CMAP.where(swpx.CMAP == 1).plot(
+    x="x", y="y", ax=ax1, vmin=0, vmax=1, cmap="turbo", add_colorbar=False
+)
+ax1.set_title("Reflectivity corr")
+
+ax1.plot(swpx.longitude.values, swpx.latitude.values, marker="*", c="r")
+
+elev.plot(x="lon", y="lat", ax=ax1, zorder=-2, cmap="terrain")
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7fba847a85b0>
+
+
+../../_images/wradlib_data_quality_29_1.png +
+
+
+
+

Use hvplot for interactive zooming and panning

+

Often it is desirable to quickly zoom and pan in the plots. Although matplotlib has that ability, it still is quite slow. Here hvplot, a holoviews based plotting framework, can be utilized. As frontend bokeh is used.

+
+
+
import hvplot
+import hvplot.xarray
+
+
+
+
+
+
+

We need to rechunk the coordinates as hvplot needs chunked variables and coords.

+

todo # vergleichbar machen mit beam blockage angle/entfernung

+
+
+
cl = (
+    swpx.CMAP.where(swpx.CMAP == 1)
+    .chunk(chunks={})
+    .hvplot.quadmesh(
+        x="x", y="y", cmap="Reds", width=800, height=700, clim=(0, 1), alpha=0.6
+    )
+)
+dm = elev.hvplot.quadmesh(
+    x="lon",
+    y="lat",
+    cmap="terrain",
+    width=800,
+    height=700,
+    clim=(0, 4000),
+    rasterize=True,
+)
+dm * cl
+
+
+
+
+
+
+
+
+
+
+
+

Convert DEM to spherical coordinates

+
+
+
sitecoords = (swpx.longitude.values, swpx.latitude.values, swpx.altitude.values)
+r = swpx.range.values
+az = swpx.azimuth.values
+bw = 0.8
+beamradius = wrl.util.half_power_radius(r, bw)
+
+
+
+
+
+
+
rastervalues, rastercoords, proj = wrl.georef.extract_raster_dataset(
+    dem, nodata=-32768.0
+)
+
+rlimits = (extent["left"], extent["bottom"], extent["right"], extent["top"])
+# Clip the region inside our bounding box
+ind = wrl.util.find_bbox_indices(rastercoords, rlimits)
+rastercoords = rastercoords[ind[1] : ind[3], ind[0] : ind[2], ...]
+rastervalues = rastervalues[ind[1] : ind[3], ind[0] : ind[2]]
+
+polcoords = np.dstack([swpx.x.values, swpx.y.values])
+# Map rastervalues to polar grid points
+polarvalues = wrl.ipol.cart_to_irregular_spline(
+    rastercoords, rastervalues, polcoords, order=3, prefilter=False
+)
+
+
+
+
+
+
+

Partial and Cumulative Beamblockage

+
+
+
PBB = wrl.qual.beam_block_frac(polarvalues, swpx.z.values, beamradius)
+PBB = np.ma.masked_invalid(PBB)
+print(PBB.shape)
+
+
+
+
+
(360, 200)
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/wradlib/qual.py:127: RuntimeWarning: invalid value encountered in sqrt
+  numer = (ya * np.sqrt(a**2 - y**2)) + (a * np.arcsin(ya)) + (np.pi * a / 2.0)
+/srv/conda/envs/notebook/lib/python3.9/site-packages/wradlib/qual.py:127: RuntimeWarning: invalid value encountered in arcsin
+  numer = (ya * np.sqrt(a**2 - y**2)) + (a * np.arcsin(ya)) + (np.pi * a / 2.0)
+
+
+
+
+
+
+
CBB = wrl.qual.cum_beam_block_frac(PBB)
+print(CBB.shape)
+
+
+
+
+
(360, 200)
+
+
+
+
+
+
+

Plotting Beamblockage

+
+
+
# just a little helper function to style x and y axes of our maps
+def annotate_map(ax, cm=None, title=""):
+    xticks = ax.get_xticks()
+    ticks = (xticks / 1000).astype(int)
+    ax.set_xticks(xticks)
+    ax.set_xticklabels(ticks)
+    yticks = ax.get_yticks()
+    ticks = (yticks / 1000).astype(int)
+    ax.set_yticks(yticks)
+    ax.set_yticklabels(ticks)
+    ax.set_xlabel("Kilometers")
+    ax.set_ylabel("Kilometers")
+    if not cm is None:
+        plt.colorbar(cm, ax=ax)
+    if not title == "":
+        ax.set_title(title)
+    ax.grid()
+
+
+
+
+
+
+
alt = swpx.z.values
+fig = plt.figure(figsize=(15, 12))
+
+# create subplots
+ax1 = plt.subplot2grid((2, 2), (0, 0))
+ax2 = plt.subplot2grid((2, 2), (0, 1))
+ax3 = plt.subplot2grid((2, 2), (1, 0), colspan=2, rowspan=1)
+
+# azimuth angle
+angle = 270
+
+# Plot terrain (on ax1)
+ax1, dem = wrl.vis.plot_ppi(
+    polarvalues, ax=ax1, r=r, az=az, cmap=mpl.cm.terrain, vmin=0.0
+)
+ax1.plot(
+    [0, np.sin(np.radians(angle)) * 1e5], [0, np.cos(np.radians(angle)) * 1e5], "r-"
+)
+ax1.plot(sitecoords[0], sitecoords[1], "ro")
+annotate_map(ax1, dem, "Terrain within {0} km range".format(np.max(r / 1000.0) + 0.1))
+ax1.set_xlim(-100000, 100000)
+ax1.set_ylim(-100000, 100000)
+
+# Plot CBB (on ax2)
+ax2, cbb = wrl.vis.plot_ppi(CBB, ax=ax2, r=r, az=az, cmap=mpl.cm.PuRd, vmin=0, vmax=1)
+annotate_map(ax2, cbb, "Beam-Blockage Fraction")
+ax2.set_xlim(-100000, 100000)
+ax2.set_ylim(-100000, 100000)
+
+# Plot single ray terrain profile on ax3
+(bc,) = ax3.plot(r / 1000.0, alt[angle, :], "-b", linewidth=3, label="Beam Center")
+(b3db,) = ax3.plot(
+    r / 1000.0,
+    (alt[angle, :] + beamradius),
+    ":b",
+    linewidth=1.5,
+    label="3 dB Beam width",
+)
+ax3.plot(r / 1000.0, (alt[angle, :] - beamradius), ":b")
+ax3.fill_between(r / 1000.0, 0.0, polarvalues[angle, :], color="0.75")
+ax3.set_xlim(0.0, np.max(r / 1000.0) + 0.1)
+ax3.set_ylim(0.0, 3000)
+ax3.set_xlabel("Range (km)")
+ax3.set_ylabel("Altitude (m)")
+ax3.grid()
+
+axb = ax3.twinx()
+(bbf,) = axb.plot(r / 1000.0, CBB[angle, :], "-g", label="BBF")
+axb.spines["right"].set_color("g")
+axb.tick_params(axis="y", colors="g")
+axb.set_ylabel("Beam-blockage fraction", c="g")
+axb.set_ylim(0.0, 1.0)
+axb.set_xlim(0.0, np.max(r / 1000.0) + 0.1)
+
+
+legend = ax3.legend(
+    (bc, b3db, bbf),
+    ("Beam Center", "3 dB Beam width", "BBF"),
+    loc="upper left",
+    fontsize=10,
+)
+
+
+
+
+../../_images/wradlib_data_quality_42_0.png +
+
+
+
+

Plotting Beamblockage on Curvelinear Grid

+

Here you get an better impression of the actual beam progression .

+
+
+
def height_formatter(x, pos):
+    x = (x - 6370000) / 1000
+    fmt_str = "{:g}".format(x)
+    return fmt_str
+
+
+def range_formatter(x, pos):
+    x = x / 1000.0
+    fmt_str = "{:g}".format(x)
+    return fmt_str
+
+
+
+
+
+
+
fig = plt.figure(figsize=(15, 8))
+
+cgax, caax, paax = wrl.vis.create_cg(fig=fig, rot=0, scale=1)
+
+# azimuth angle
+angle = 270
+
+# fix grid_helper
+er = 6370000
+gh = cgax.get_grid_helper()
+gh.grid_finder.grid_locator2._nbins = 80
+gh.grid_finder.grid_locator2._steps = [1, 2, 4, 5, 10]
+
+# calculate beam_height and arc_distance for ke=1
+# means line of sight
+bhe = wrl.georef.bin_altitude(r, 0, sitecoords[2], re=er, ke=1.0)
+ade = wrl.georef.bin_distance(r, 0, sitecoords[2], re=er, ke=1.0)
+nn0 = np.zeros_like(r)
+# for nice plotting we assume earth_radius = 6370000 m
+ecp = nn0 + er
+# theta (arc_distance sector angle)
+thetap = -np.degrees(ade / er) + 90.0
+
+# zero degree elevation with standard refraction
+bh0 = wrl.georef.bin_altitude(r, 0, sitecoords[2], re=er)
+
+# plot (ecp is earth surface normal null)
+(bes,) = paax.plot(thetap, ecp, "-k", linewidth=3, label="Earth Surface NN")
+(bc,) = paax.plot(thetap, ecp + alt[angle, :], "-b", linewidth=3, label="Beam Center")
+(bc0r,) = paax.plot(thetap, ecp + bh0, "-g", label="0 deg Refraction")
+(bc0n,) = paax.plot(thetap, ecp + bhe, "-r", label="0 deg line of sight")
+(b3db,) = paax.plot(
+    thetap, ecp + alt[angle, :] + beamradius, ":b", label="+3 dB Beam width"
+)
+paax.plot(thetap, ecp + alt[angle, :] - beamradius, ":b", label="-3 dB Beam width")
+
+# orography
+paax.fill_between(thetap, ecp, ecp + polarvalues[angle, :], color="0.75")
+
+# shape axes
+cgax.set_xlim(0, np.max(ade))
+cgax.set_ylim([ecp.min() - 1000, ecp.max() + 2500])
+caax.grid(True, axis="x")
+cgax.grid(True, axis="y")
+cgax.axis["top"].toggle(all=False)
+caax.yaxis.set_major_locator(
+    mpl.ticker.MaxNLocator(steps=[1, 2, 4, 5, 10], nbins=20, prune="both")
+)
+caax.xaxis.set_major_locator(mpl.ticker.MaxNLocator())
+caax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(height_formatter))
+caax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(range_formatter))
+
+caax.set_xlabel("Range (km)")
+caax.set_ylabel("Altitude (km)")
+
+legend = paax.legend(
+    (bes, bc0n, bc0r, bc, b3db),
+    (
+        "Earth Surface NN",
+        "0 deg line of sight",
+        "0 deg std refraction",
+        "Beam Center",
+        "3 dB Beam width",
+    ),
+    loc="lower left",
+    fontsize=10,
+)
+
+
+
+
+../../_images/wradlib_data_quality_45_0.png +
+
+
+
+
+

Use Clutter and Beamblockage as Quality Index

+

Simple masking with cumulative beam blockage and Gabella.

+
+
+
swpx = swpx.assign({"CBB": (["azimuth", "range"], CBB)})
+# recalculate georeferencing for AEQD
+swpx = swpx.pipe(wrl.georef.georeference_dataset)
+
+
+
+
+
+
+
fig = plt.figure(figsize=(12, 4))
+
+ax1 = fig.add_subplot(121)
+swpx.reflectivity.plot(x="x", y="y", ax=ax1, cmap="turbo", vmin=0, vmax=60)
+ax1.set_title(f"Signal Processor - {swpx.time.values.astype('M8[s]')}")
+ax1.set_aspect("equal")
+
+ax2 = fig.add_subplot(122)
+# CBB > 0.5, CMAP == 1, RHOHV < 0.8 is masked
+swpx.where(
+    (swpx.CBB <= 0.5)
+    & (swpx.CMAP < 1.0)
+    & (swpx.uncorrected_cross_correlation_ratio >= 0.8)
+).reflectivity_hh_clut.plot(x="x", y="y", ax=ax2, cmap="turbo", vmin=0, vmax=60)
+ax2.set_title(f"Gabella+CBB+RHOHV - {swpx.time.values.astype('M8[s]')}")
+ax2.set_aspect("equal")
+
+
+
+
+../../_images/wradlib_data_quality_48_0.png +
+
+
+
+
+

Summary

+

We’ve just learned how to use \(\omega radlib\)’s Gabella clutter detection for single sweeps. Wrapping numpy based functions for use with xarray.apply_ufunc has been shown. We’ve looked into digital elevation maps and beam blockage calculations.

+
+

What’s next?

+

In the next notebook we dive into processing of differential phase.

+
+
+ +
+ + + + +
+ +
+ + +
+

previous

+

wradlib radar data io, visualisation, gridding and gis export

+
+ + +
+

next

+

wradlib Phase Processing - System PhiDP - ZPHI-Method

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/wradlib/wradlib_differential_phase.html b/_preview/104/notebooks/wradlib/wradlib_differential_phase.html new file mode 100644 index 00000000..6ba4a342 --- /dev/null +++ b/_preview/104/notebooks/wradlib/wradlib_differential_phase.html @@ -0,0 +1,2658 @@ + + + + + + + + wradlib Phase Processing - System PhiDP - ZPHI-Method — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

wradlib logo png

+
+

wradlib Phase Processing - System PhiDP - ZPHI-Method

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. Reading sweep data into xarray based Dataset

    2. +

  2. Retrieval of system PhiDP

    3. +

  3. ZPHI Phase processing

    4. +

  4. Attenuation correction using specific Attenuation

    5. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Xarray Basics

Helpful

Basic Dataset/DataArray

Matplotlib Basics

Helpful

Basic Plotting

Intro to Cartopy

Helpful

Projections

+
    +

  • Time to learn: 10 minutes

    • +
+
+
+
import datetime as dt
+import glob
+import os
+import sys
+import warnings
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from scipy.integrate import cumulative_trapezoid
+
+import wradlib as wrl
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

Import data

+

As a quick example to show the algorithm, we use a file from Down Under. For the further processing we us XBand data from BoXPol research radar at the University of Bonn, Germany.

+
+
+
boxpol = "data/hdf5/boxpol/2014-11-16--03:45:00,00.mvol"
+terrey = "data/hdf5/terrey_39.h5"
+
+
+
+
+
+
+
swp0 = wrl.io.open_odim_dataset(terrey)[0]
+swp0 = swp0.pipe(wrl.georef.georeference_dataset)
+
+
+
+
+
+
+

Pre-Processing

+

System PHIDP aka Phase Offset

+

The following function returns phase offset as well as start and stop ranges of the region of interest (first precipitating bins).

+
+
+
def phase_offset(phioff, method=None, rng=3000.0, npix=None, **kwargs):
+    """Calculate Phase offset.
+
+    Parameter
+    ---------
+    phioff : xarray.DataArray
+        differential phase DataArray
+
+    Keyword Arguments
+    -----------------
+    method : str
+        aggregation method, defaults to 'median'
+    rng : float
+        range in m to calculate system phase offset
+
+    Return
+    ------
+    phidp_offset : xarray.Dataset
+        Dataset with PhiDP offset and start/stop ranges
+    """
+    range_step = np.diff(phioff.range)[0]
+    nprec = int(rng / range_step)
+    if nprec % 2:
+        nprec += 1
+
+    if npix is None:
+        npix = nprec // 2 + 1
+
+    # create binary array
+    phib = xr.where(np.isnan(phioff), 0, 1)
+
+    # take nprec range bins and calculate sum
+    phib_sum = phib.rolling(range=nprec, **kwargs).sum(skipna=True)
+
+    # find at least N pixels in
+    # phib_sum_N = phib_sum.where(phib_sum >= npix)
+    phib_sum_N = xr.where(phib_sum <= npix, phib_sum, npix)
+
+    # get start range of first N consecutive precip bins
+    start_range = (
+        phib_sum_N.idxmax(dim="range") - nprec // 2 * np.diff(phib_sum.range)[0]
+    )
+    start_range = xr.where(start_range < 0, 0, start_range)
+
+    # get stop range
+    stop_range = start_range + rng
+    # get phase values in specified range
+    off = phioff.where(
+        (phioff.range >= start_range) & (phioff.range <= stop_range), drop=False
+    )
+    # calculate nan median over range
+    if method is None:
+        method = "median"
+    func = getattr(off, method)
+    off_func = func(dim="range", skipna=True)
+
+    return xr.Dataset(
+        dict(
+            PHIDP_OFFSET=off_func,
+            start_range=start_range,
+            stop_range=stop_range,
+            phib_sum=phib_sum,
+            phib=phib,
+        )
+    )
+
+
+
+
+
+
+

Example Showcase

+
+
+
dr_m = swp0.range.diff("range").median()
+swp_msk = swp0.where((swp0.DBZH >= 0.0))
+swp_msk = swp_msk.where(swp_msk.RHOHV > 0.8)
+swp_msk = swp_msk.where(swp_msk.range > dr_m * 5)
+
+phi_masked = swp_msk.PHIDP.copy()
+off = phase_offset(
+    phi_masked, method="median", rng=2000.0, npix=7, center=True, min_periods=4
+)
+phioff = off.PHIDP_OFFSET.median(dim="azimuth", skipna=True)
+
+
+
+
+
+
+
fig = plt.figure(figsize=(16, 7))
+ax1 = plt.subplot(111, projection="polar")
+# set the lable go clockwise and start from the top
+ax1.set_theta_zero_location("N")
+# clockwise
+ax1.set_theta_direction(-1)
+theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)
+ax1.plot(theta, off.PHIDP_OFFSET, color="b", linewidth=3)
+
+ax1.plot(theta, np.ones_like(theta) * phioff.values, color="r", lw=2)
+ti = off.time.values.astype("M8[s]")
+om = phioff.values
+tx = ax1.set_title(f"{ti}\n" + r"$\phi_{DP}-Offset$ " + f"{om:.1f} (deg)")
+tx.set_y(1.1)
+xticks = ax1.set_xticks(np.pi / 180.0 * np.linspace(0, 360, 36, endpoint=False))
+ax1.set_ylim(50, 150)
+
+
+
+
+
(50.0, 150.0)
+
+
+../../_images/wradlib_differential_phase_15_1.png +
+
+
+
+
fig = plt.figure(figsize=(18, 5))
+swp_msk.DBZH.plot(x="azimuth")
+off.start_range.plot(c="b", lw=2)
+off.stop_range.plot(c="r", lw=2)
+plt.gca().set_ylim(0, 25000)
+
+
+
+
+
(0.0, 25000.0)
+
+
+../../_images/wradlib_differential_phase_16_1.png +
+
+
+
+

Process BoXPol data

+
+
+
vol = wrl.io.open_gamic_dataset(boxpol)
+
+
+
+
+
+
+
display(vol)
+
+
+
+
+
<wradlib.RadarVolume>
+Dimension(s): (sweep: 1)
+Elevation(s): (1.5)
+
+
+
+
+
+
+
swp = vol[0].copy()
+swp = swp.pipe(wrl.georef.georeference_dataset)
+
+
+
+
+
+
+
display(swp)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 1000)
+Coordinates: (12/15)
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+  * range       (range) float32 50.0 150.0 250.0 ... 9.985e+04 9.995e+04
+    elevation   (azimuth) float64 1.505 1.505 1.505 1.505 ... 1.505 1.505 1.505
+    rtime       (azimuth) datetime64[ns] 2014-11-16T03:48:49 ... 2014-11-16T0...
+    time        datetime64[ns] 2014-11-16T03:48:38
+    sweep_mode  <U20 'azimuth_surveillance'
+    ...          ...
+    x           (azimuth, range) float64 0.4362 1.309 2.181 ... -870.7 -871.6
+    y           (azimuth, range) float64 49.98 149.9 ... 9.978e+04 9.988e+04
+    z           (azimuth, range) float64 100.9 103.5 ... 3.309e+03 3.313e+03
+    gr          (azimuth, range) float64 49.98 149.9 ... 9.978e+04 9.988e+04
+    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
+    bins        (azimuth, range) float32 50.0 150.0 ... 9.985e+04 9.995e+04
+Data variables:
+    KDP         (azimuth, range) float32 ...
+    PHIDP       (azimuth, range) float32 ...
+    DBZH        (azimuth, range) float32 ...
+    DBZV        (azimuth, range) float32 ...
+    RHOHV       (azimuth, range) float32 ...
+    DBTH        (azimuth, range) float32 ...
+    DBTV        (azimuth, range) float32 ...
+    VRADH       (azimuth, range) float32 ...
+    VRADV       (azimuth, range) float32 ...
+    WRADH       (azimuth, range) float32 ...
+    WRADV       (azimuth, range) float32 ...
+    ZDR         (azimuth, range) float32 ...
+Attributes:
+    fixed_angle:  1.5
+
+
+

Create Plot

+
+
+
fig = plt.figure(figsize=(13, 5))
+
+ax1 = fig.add_subplot(121)
+im1 = swp.PHIDP.where(swp.RHOHV > 0.8).plot(x="x", y="y", ax=ax1, cmap="turbo")
+t = plt.title(r"Uncorrected $\phi_{DP}$")
+t.set_y(1.1)
+
+ax2 = fig.add_subplot(122)
+im2 = swp.DBZH.where(swp.RHOHV > 0.8).plot(
+    x="x", y="y", ax=ax2, cmap="turbo", vmin=-10, vmax=50
+)
+t = plt.title(r"Uncorrected $Z_{H}$")
+t.set_y(1.1)
+fig.suptitle(swp.time.values, fontsize=14)
+fig.subplots_adjust(wspace=0.25)
+
+
+
+
+../../_images/wradlib_differential_phase_23_0.png +
+
+
+
+

Apply reasonable masking

+
+
+
dr_m = swp.range.diff("range").median()
+swp_msk = swp.where((swp.DBZH >= 0.0))
+swp_msk = swp_msk.where(swp_msk.RHOHV > 0.8)
+swp_msk = swp_msk.where(swp_msk.range > dr_m * 2)
+
+
+phi_masked = swp_msk.PHIDP.copy()
+off = phase_offset(
+    phi_masked, method="median", rng=2000.0, npix=7, center=True, min_periods=2
+)
+phioff = off.PHIDP_OFFSET.median(dim="azimuth", skipna=True)
+
+
+
+
+
+
+

Plot phase offset distribution

+
+
+
fig = plt.figure(figsize=(16, 7))
+ax1 = plt.subplot(111, projection="polar")
+# set the lable go clockwise and start from the top
+ax1.set_theta_zero_location("N")
+# clockwise
+ax1.set_theta_direction(-1)
+theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)
+ax1.plot(theta, off.PHIDP_OFFSET, color="b", linewidth=3)
+
+ax1.plot(theta, np.ones_like(theta) * phioff.values, color="r", lw=2)
+ti = off.time.values.astype("M8[s]")
+om = phioff.values
+tx = ax1.set_title(f"{ti}\n" + r"$\phi_{DP}-Offset$ " + f"{om:.1f} (deg)")
+tx.set_y(1.1)
+xticks = ax1.set_xticks(np.pi / 180.0 * np.linspace(0, 360, 36, endpoint=False))
+ax1.set_ylim(-120, -70)
+
+
+
+
+
(-120.0, -70.0)
+
+
+../../_images/wradlib_differential_phase_27_1.png +
+
+
+
+
fig = plt.figure(figsize=(18, 5))
+swp_msk.DBZH.plot(x="azimuth")
+off.start_range.plot(c="b", lw=2)
+off.stop_range.plot(c="r", lw=2)
+plt.gca().set_ylim(0, 10000)
+
+
+
+
+
(0.0, 10000.0)
+
+
+../../_images/wradlib_differential_phase_28_1.png +
+
+

Pleaser refer to the ZPHI-Method section at the bottom of this notebook for references and equations.

+
+
+

Retrieving \(\Delta \phi_{DP}\)

+

We will use the simple method of finding the first and the last non NAN values per ray from \(\phi_{DP}^{corr}\).

+

This is the most simple and probably not very robust method.

+
+
+
def phase_zphi(phi, rng=1000.0, **kwargs):
+    range_step = np.diff(phi.range)[0]
+
+    nprec = int(rng / range_step)
+
+    if nprec % 2:
+        nprec += 1
+
+    # create binary array
+    phib = xr.where(np.isnan(phi), 0, 1)
+
+    # take nprec range bins and calculate sum
+    phib_sum = phib.rolling(range=nprec, **kwargs).sum(skipna=True)
+
+    offset = nprec // 2 * np.diff(phib_sum.range)[0]
+    offset_idx = nprec // 2
+
+    start_range = phib_sum.idxmax(dim="range") - offset
+    start_range_idx = phib_sum.argmax(dim="range") - offset_idx
+
+    stop_range = phib_sum[:, ::-1].idxmax(dim="range") - offset
+    stop_range_idx = (
+        len(phib_sum.range) - (phib_sum[:, ::-1].argmax(dim="range") - offset_idx) - 2
+    )
+
+    # get phase values in specified range
+    first = phi.where(
+        (phi.range >= start_range) & (phi.range <= start_range + rng), drop=True
+    ).quantile(0.15, dim="range", skipna=True)
+    last = phi.where(
+        (phi.range >= stop_range - rng) & (phi.range <= stop_range), drop=True
+    ).quantile(0.95, dim="range", skipna=True)
+
+    return xr.Dataset(
+        dict(
+            phib=phib_sum,
+            offset=offset,
+            offset_idx=offset_idx,
+            start_range=start_range,
+            stop_range=stop_range,
+            first=first.drop("quantile"),
+            first_idx=start_range_idx,
+            last=last.drop("quantile"),
+            last_idx=stop_range_idx,
+        )
+    )
+
+
+
+
+

Apply extraction of phase parameters.

+
+
+
cphase = phase_zphi(swp_msk.PHIDP, rng=2000.0, center=True, min_periods=7)
+
+
+
+
+

Apply azimuthal averaging.

+
+
+
cphase = (
+    cphase.pad(pad_width={"azimuth": 2}, mode="wrap")
+    .rolling(azimuth=5, center=True)
+    .median(skipna=True)
+    .isel(azimuth=slice(2, -2))
+)
+
+
+
+
+
+
+

\(\Delta \phi_{DP}\) - Polar Plots

+

This visualizes first and last indizes including \(\Delta \phi_{DP}\).

+
+
+
dphi = cphase.last - cphase.first
+dphi = dphi.where(dphi >= 0).fillna(0)
+
+
+
+
+
+
+
fig = plt.figure(figsize=(20, 9))
+ax1 = plt.subplot(131, projection="polar")
+ax2 = plt.subplot(132, projection="polar")
+ax3 = plt.subplot(133, projection="polar")
+# set the lable go clockwise and start from the top
+ax1.set_theta_zero_location("N")
+ax2.set_theta_zero_location("N")
+ax3.set_theta_zero_location("N")
+# clockwise
+ax1.set_theta_direction(-1)
+ax2.set_theta_direction(-1)
+ax3.set_theta_direction(-1)
+theta = np.linspace(0, 2 * np.pi, num=360, endpoint=False)
+ax1.plot(theta, cphase.start_range, color="b", linewidth=2)
+ax1.plot(theta, cphase.stop_range, color="r", linewidth=2)
+_ = ax1.set_title("Start/Stop Range")
+
+ax2.plot(theta, cphase.first, color="b", linewidth=2)
+ax2.plot(theta, cphase.last, color="r", linewidth=2)
+_ = ax2.set_title("Start/Stop PHIDP")
+ax2.set_ylim(-110, -40)
+
+ax3.plot(theta, dphi, color="g", linewidth=3)
+# ax3.plot(theta, dphi_old, color="k", linewidth=1)
+_ = ax3.set_title("Delta PHIDP")
+
+
+
+
+../../_images/wradlib_differential_phase_38_0.png +
+
+
+
+

Calculating \(f\Delta\phi_{DP}\)

+
+\[f\Delta\phi_{DP} = 10^{0.1 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}} - 1\]
+
+
+
# todo: cband coeffizienten
+alphax = 0.28
+betax = 0.05
+bx = 0.78
+# need to expand alphax to dphi-shape
+fdphi = 10 ** (0.1 * bx * alphax * dphi) - 1
+fdphi
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (azimuth: 360)>
+array([1.50772944, 1.621746  , 1.63263211, 1.59938947, 1.77879842,
+       1.87674877, 1.91353846, 1.96305219, 1.96305219, 1.97124872,
+       2.29083108, 2.25196971, 2.31872025, 2.39724185, 2.37292792,
+       2.49336399, 2.49336399, 2.4702802 , 2.05962546, 2.05962546,
+       1.98779264, 2.08415043, 2.22085707, 2.33125884, 2.6826384 ,
+       2.81518698, 2.85859008, 2.86824995, 2.85859008, 3.03636579,
+       3.18973989, 3.43876111, 3.89274439, 3.89274439, 4.04998114,
+       3.7410609 , 3.5279313 , 3.29545116, 3.13226527, 2.63714176,
+       2.68060436, 2.68060436, 2.63714176, 2.6171011 , 2.68569016,
+       2.42366977, 2.41700817, 2.38506371, 2.17667475, 2.11841838,
+       1.87436571, 1.71911294, 1.76150461, 1.87436571, 2.01607107,
+       2.01607107, 2.04992121, 2.0311066 , 2.01923888, 1.77006176,
+       1.88550466, 1.85852905, 1.88630187, 1.93616259, 1.94184574,
+       1.94184574, 1.88550356, 1.88494586, 1.87801993, 1.87801993,
+       1.98605966, 1.96387083, 1.98770987, 2.13569607, 2.00903912,
+       2.00903912, 2.06478535, 2.02759084, 1.92099396, 1.90831172,
+       1.87333389, 1.8936456 , 1.8936456 , 2.05362955, 2.0340373 ,
+       2.36548218, 2.40602815, 2.40602815, 2.39442893, 2.43024869,
+       2.40475917, 2.35513227, 2.38211962, 2.38960217, 2.68670986,
+       2.4064985 , 2.76909239, 2.76909239, 2.44544354, 2.39161573,
+...
+       1.48493842, 1.40258469, 1.35526881, 1.35526881, 1.36570186,
+       1.36570186, 1.41723047, 1.45288304, 1.43734691, 1.43734691,
+       1.3127157 , 1.18417608, 1.20235217, 1.17394212, 1.17394212,
+       1.27155794, 1.1853823 , 1.30506194, 1.35140081, 1.39781004,
+       1.4092306 , 1.42928047, 1.41776486, 1.40155549, 1.43562991,
+       1.3748696 , 1.41656282, 1.3748696 , 1.3429393 , 1.31783232,
+       1.19242757, 1.14515811, 1.13333828, 1.12173051, 1.14116148,
+       1.14836012, 1.21945297, 1.22129306, 1.3589155 , 1.34099845,
+       1.32938614, 1.38209702, 1.34488177, 1.28477377, 1.27847183,
+       1.32231864, 1.39661884, 1.47261211, 1.47261211, 1.40856539,
+       1.45742654, 1.4808222 , 1.64794967, 1.53695988, 1.51025952,
+       1.38473063, 1.38078131, 1.37093571, 1.34734462, 1.30716441,
+       1.26344584, 1.34410378, 1.34734462, 1.39605621, 1.67293704,
+       1.77450348, 1.81270428, 1.91514859, 1.84828094, 1.93210992,
+       1.83807067, 1.93210992, 1.94916881, 1.97239827, 1.91192921,
+       1.93486535, 1.91192921, 1.94949493, 1.91353846, 2.13396412,
+       2.1055235 , 1.96059651, 1.86960552, 1.96059651, 1.89748485,
+       1.80843354, 1.85056417, 1.74022627, 1.80525399, 1.80215598,
+       1.66482719, 1.64868023, 1.62798185, 1.45695102, 1.47261211,
+       1.28540588, 1.26341505, 1.26341505, 1.30888565, 1.28414358])
+Coordinates:
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float64 1.505 1.505 1.505 1.505 ... 1.505 1.505 1.505
+    rtime       (azimuth) datetime64[ns] 2014-11-16T03:48:49 ... 2014-11-16T0...
+    time        datetime64[ns] 2014-11-16T03:48:38
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 7.072
+    latitude    float64 50.73
+    altitude    float64 99.5
+
+
+
+

Calculating Reflectivity Integrals/Sums

+
+\[za(r) = \left[Z_a(r) \right ]^b\]
+
+\[iza(r,r2) = 0.46 \cdot b \cdot \int_{r}^{r2} \left [Z_a(s) \right ]^b ds\]
+

We do not restrict (mask) the reflectivities for now, but switch between DBTH and DBZH to see the difference.

+
+
+
zhraw = swp.DBZH.where(
+    (swp.range > cphase.start_range) & (swp.range < cphase.stop_range)
+)
+zhraw.plot(x="x", y="y", cmap="turbo", vmin=0, vmax=100)
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f39e8370a00>
+
+
+../../_images/wradlib_differential_phase_43_1.png +
+
+
+
+
# calculate linear reflectivity and ^b
+zax = zhraw.pipe(wrl.trafo.idecibel).fillna(0)
+za = zax**bx
+# set masked to zero for integration
+za_zero = za.fillna(0)
+
+
+
+
+

Calculate cumulative integral, and subtract from maximum. That way we have the cumulative sum for every bin until the end of the ray.

+
+
+
def cumulative_trapezoid_xarray(da, dim, initial=0):
+    """Intgration with the scipy.integrate.cumtrapz.
+
+    Parameter
+    ---------
+    da : xarray.DataArray
+        array with differential phase data
+    dim : int
+        size of window in range dimension
+
+    Keyword Arguments
+    -----------------
+    initial : float
+        minimum number of valid bins
+
+    Return
+    ------
+    kdp : xarray.DataArray
+        DataArray with specific differential phase values
+    """
+    x = da[dim]
+    dx = x.diff(dim).median(dim).values
+    if x.attrs["units"] == "meters":
+        dx /= 1000.0
+    return xr.apply_ufunc(
+        cumulative_trapezoid,
+        da,
+        input_core_dims=[[dim]],
+        output_core_dims=[[dim]],
+        dask="parallelized",
+        kwargs=dict(axis=da.get_axis_num(dim), initial=initial, dx=dx),
+        dask_gufunc_kwargs=dict(allow_rechunk=True),
+    )
+
+
+
+
+
+
+
iza_x = 0.46 * bx * za_zero.pipe(cumulative_trapezoid_xarray, "range", initial=0)
+iza = iza_x.max("range") - iza_x
+
+
+
+
+
+
+

Calculating Attenuation \(A_{H}\) for whole domain

+
+\[A_{H}(r) = \frac{\left [Z_a(r) \right ]^b \cdot f(\Delta \phi_{DP})}{0.46b \int_{r1}^{r2} \left [Z_a(s) \right ]^b ds + f(\Delta \phi_{DP}) \cdot 0.46b \int_{r}^{r2} \left [Z_a(s) \right ]^b ds}\]
+

We can reduce the number of operations by rearranging the equation like this:

+
+\[A_{H}(r) = \frac{\left [Z_a(r) \right ]^b}{\frac{0.46b \int_{r1}^{r2} \left [Z_a(s) \right ]^b ds}{f(\Delta \phi_{DP})} + 0.46b \int_{r}^{r2} \left [Z_a(s) \right ]^b ds}\]
+
+
+
iza_fdphi = iza / fdphi
+idx = cphase.first_idx.astype(int)
+iza_first = iza_fdphi[:, idx]
+ah = za / (iza_first + iza)
+
+
+
+
+

Give it a name!

+
+
+
ah.name = "AH"
+ah.attrs["short_name"] = "specific_attenuation_h"
+ah.attrs["long_name"] = "Specific attenuation H"
+ah.attrs["units"] = "dB/km"
+
+
+
+
+
+
+
fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111)
+ticks_ah = np.arange(0, 5, 0.2)
+im = ah.plot(x="x", y="y", ax=ax, cmap="turbo", levels=np.arange(0, 0.5, 0.025))
+
+
+
+
+../../_images/wradlib_differential_phase_53_0.png +
+
+
+
+

Calculate \(\phi_{DP}^{cal}(r, \alpha)\) for whole domain

+
+\[\phi_{DP}^{cal}(r_i, \alpha) = 2 \cdot \int_{r1}^{r2} \frac{A_H(s; \alpha)}{\alpha}ds\]
+
+
+
phical = 2 * (ah / alphax).pipe(cumulative_trapezoid_xarray, "range", initial=0)
+phical.name = "PHICAL"
+phical.attrs = wrl.io.xarray.moments_mapping["PHIDP"]
+
+
+
+
+
+
+
phical.where(swp_msk.PHIDP).plot(x="x", y="y", vmin=0, vmax=50, cmap="turbo")
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f39e820d340>
+
+
+../../_images/wradlib_differential_phase_56_1.png +
+
+
+
+

Apply attenuation correction

+
+
+
print(alphax)
+
+
+
+
+
0.28
+
+
+
+
+
+
+
zhraw = swp.DBZH.copy()
+zdrraw = swp.ZDR.copy()
+
+
+
+
+
+
+
with xr.set_options(keep_attrs=True):
+    zhcorr = zhraw + alphax * (phical)
+    zdiff = zhcorr - zhraw
+    zdrcorr = zdrraw + betax * (phical)
+    zdrdiff = zdrcorr - zdrraw
+
+
+
+
+
+
+
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
+    nrows=2,
+    ncols=2,
+    figsize=(15, 12),
+    sharex=True,
+    sharey=True,
+    squeeze=True,
+    constrained_layout=True,
+)
+
+scantime = zhraw.time.values.astype("<M8[s]")
+t = fig.suptitle(scantime, fontsize=14)
+t.set_y(1.05)
+
+zhraw.plot(x="x", y="y", ax=ax1, cmap="turbo", levels=np.arange(0, 40, 2))
+ax1.set_title(r"Uncorrected $Z_{H}$", fontsize=16)
+zhcorr.plot(x="x", y="y", ax=ax2, cmap="turbo", levels=np.arange(0, 40, 2))
+ax2.set_title(r"Corrected $Z_{H}$", fontsize=16)
+
+zdrraw.plot(x="x", y="y", ax=ax3, cmap="turbo", levels=np.arange(-0.5, 3, 0.1))
+ax3.set_title(r"Uncorrected $Z_{DR}$", fontsize=16)
+zdrcorr.plot(x="x", y="y", ax=ax4, cmap="turbo", levels=np.arange(-0.5, 3, 0.1))
+ax4.set_title(r"Corrected $Z_{DR}$", fontsize=16)
+
+
+
+
+
Text(0.5, 1.0, 'Corrected $Z_{DR}$')
+
+
+../../_images/wradlib_differential_phase_61_1.png +
+
+
+
+
+

\(K_{DP}\) from \(A_H\) vs. \(K_{DP}\) from \(\phi_{DP}\)

+
    +

  • \(K_{DP} = \frac{A_H}{\alpha}\)

    • +

  • \(K_{DP} = \frac{1}{2}\frac{\mathrm{d}\phi_{DP}}{\mathrm{d}r}\)

    • +
+

What are the benefits of \(K_{DP}(A_H)\)?

+
    +

  • no noise artefacts

    • +

  • no \(\delta\)

    • +

  • no negative \(K_{DP}\)

    • +

  • no spatial degradation

    • +
+
+
+
def kdp_from_phidp(da, winlen, min_periods=2):
+    """Derive KDP from PHIDP (based on convolution filter).
+
+    Parameter
+    ---------
+    da : xarray.DataArray
+        array with differential phase data
+    winlen : int
+        size of window in range dimension
+
+    Keyword Arguments
+    -----------------
+    min_periods : int
+        minimum number of valid bins
+
+    Return
+    ------
+    kdp : xarray.DataArray
+        DataArray with specific differential phase values
+    """
+    dr = da.range.diff("range").median("range").values / 1000.0
+    print("range res [km]:", dr)
+    print("processing window [km]:", dr * winlen)
+    return xr.apply_ufunc(
+        wrl.dp.kdp_from_phidp,
+        da,
+        input_core_dims=[["range"]],
+        output_core_dims=[["range"]],
+        dask="parallelized",
+        kwargs=dict(winlen=winlen, dr=dr, min_periods=min_periods),
+        dask_gufunc_kwargs=dict(allow_rechunk=True),
+    )
+
+
+def kdp_phidp_vulpiani(da, winlen, min_periods=2):
+    """Derive KDP from PHIDP (based on Vulpiani).
+
+    ParameterRHOHV_NC
+    ---------
+    da : xarray.DataArray
+        array with differential phase data
+    winlen : int
+        size of window in range dimension
+
+    Keyword Arguments
+    -----------------
+    min_periods : int
+        minimum number of valid bins
+
+    Return
+    ------
+    kdp : xarray.DataArray
+        DataArray with specific differential phase values
+    """
+    dr = da.range.diff("range").median("range").values / 1000.0
+    print("range res [km]:", dr)
+    print("processing window [km]:", dr * winlen)
+    return xr.apply_ufunc(
+        wrl.dp.process_raw_phidp_vulpiani,
+        da,
+        input_core_dims=[["range"]],
+        output_core_dims=[["range"], ["range"]],
+        dask="parallelized",
+        kwargs=dict(winlen=winlen, dr=dr, min_periods=min_periods),
+        dask_gufunc_kwargs=dict(allow_rechunk=True),
+    )
+
+
+
+
+
+
+
%%time
+kdp1 = kdp_from_phidp(swp_msk.PHIDP, winlen=31, min_periods=11)
+kdp1.attrs = wrl.io.xarray.moments_mapping["KDP"]
+
+kdp2 = kdp_phidp_vulpiani(swp.PHIDP, winlen=71, min_periods=21)[1]
+kdp2.attrs = wrl.io.xarray.moments_mapping["KDP"]
+
+kdp3 = xr.zeros_like(kdp1)
+kdp3.attrs = wrl.io.xarray.moments_mapping["KDP"]
+kdp3.data = ah / alphax
+
+
+
+
+
range res [km]: 0.1
+processing window [km]: 3.1
+
+
+
range res [km]: 0.1
+processing window [km]: 7.1000000000000005
+CPU times: user 326 ms, sys: 59.9 ms, total: 386 ms
+Wall time: 385 ms
+
+
+
+
+
+
+
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
+    nrows=2, ncols=2, figsize=(12, 10), constrained_layout=True
+)
+
+swp.KDP.plot(
+    x="x",
+    y="y",
+    ax=ax1,
+    cmap="turbo",
+    levels=np.arange(-0.5, 1, 0.1),
+    cbar_kwargs=dict(shrink=0.64),
+)
+ax1.set_title(r"$K_{DP}$ - Signalprocessor", fontsize=16)
+ax1.set_aspect("equal")
+kdp1.plot(
+    x="x",
+    y="y",
+    ax=ax2,
+    cmap="turbo",
+    levels=np.arange(-0.5, 1, 0.1),
+    cbar_kwargs=dict(shrink=0.64),
+)
+ax2.set_title(r"$K_{DP}$ - Simple Derivative", fontsize=16)
+ax2.set_aspect("equal")
+kdp2.plot(
+    x="x",
+    y="y",
+    ax=ax3,
+    cmap="turbo",
+    levels=np.arange(-0.5, 1, 0.1),
+    cbar_kwargs=dict(shrink=0.64),
+)
+ax3.set_title(r"$K_{DP}$ - Vulpiani", fontsize=16)
+ax3.set_aspect("equal")
+kdp3.plot(
+    x="x",
+    y="y",
+    ax=ax4,
+    cmap="turbo",
+    levels=np.arange(-0.5, 1, 0.1),
+    cbar_kwargs=dict(shrink=0.64),
+)
+ax4.set_title(r"$K_{DP}$ - spec. Attenuation/ZPHI", fontsize=16)
+ax4.set_aspect("equal")
+
+
+
+
+../../_images/wradlib_differential_phase_65_0.png +
+
+
+
+
+

Summary

+

We’ve just learned how to derive System Phase Offset and Specific Attenuation AH using the ZPHI-Method. Different KDP derivation methods have been compared.

+
+

What’s next?

+

In the next notebook we dive into Quasi Vertical Profiles.

+
+
+
+

Resources and references

+
    +

  • xarray

    • +

  • dask

    • +

  • wradlib xarray backends

    • +

  • OPERA ODIM_H5

    • +

  • WMO JET-OWR

    • +

  • Testud, J., Le Bouar, E., Obligis, E., & Ali-Mehenni, M. (2000). The Rain Profiling Algorithm Applied to Polarimetric Weather Radar, Journal of Atmospheric and Oceanic Technology, 17(3), 332-356. Retrieved Nov 24, 2021, from https://journals.ametsoc.org/view/journals/atot/17/3/1520-0426_2000_017_0332_trpaat_2_0_co_2.xml

    • +

  • Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., & Trömel, S. (2015). Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths.: Part I: Radar Calibration and Partial Beam Blockage Estimation. Journal of Hydrometeorology, 16(2), 487–502. http://www.jstor.org/stable/24914953

    • +
+
+

ZPHI-Method

+

see Testud et.al. (chapter 4. p. 339ff.), Diederich et.al. (chapter 3. p. 492 ff).

+

There is a equational difference in the two papers, which can be solved like this:

+

\(\begin{equation} +f\Delta\phi_{DP} = 10^{0.1 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}} - 1 +\tag{1} +\end{equation}\)

+

\(\begin{equation} +C(b, PIA) = \exp[{0.23 \cdot b \cdot (PIA)}] - 1 +\tag{2} +\end{equation}\)

+

with

+

\(\begin{equation} +PIA = \alpha \cdot \Delta\phi_{DP} +\tag{3} +\end{equation}\)

+

\(\begin{equation} +C(b, PIA) = \exp[{0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}] - 1 +\tag{4} +\end{equation}\)

+

Both expressions are used equivalently:

+

\(\begin{equation} +10^{0.1 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}} - 1 = \exp[{0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}] - 1 +\tag{5} +\end{equation}\)

+

Using logarithmic identities:

+

\(\begin{equation} +\ln {u^r} = r \cdot \ln {u} +\tag{6a} +\end{equation}\)

+

\(\begin{equation} +\exp {\ln x} = x +\tag{6b} +\end{equation}\)

+

the left hand side can be further expressed as:

+

\(\begin{equation} +\exp [\ln {10^{0.1 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}}] - 1 = \exp[{0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}] - 1 +\tag{7a} +\end{equation}\)

+

\(\begin{equation} +\exp[0.1 \cdot b \cdot \alpha \cdot \Delta\phi_{DP} \cdot \ln {10}] - 1 = \exp[{0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}] - 1 +\tag{7b} +\end{equation}\)

+

leading to equality

+

\(\begin{equation} +\exp[0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}] - 1 = \exp[{0.23 \cdot b \cdot \alpha \cdot \Delta\phi_{DP}}] - 1 +\tag{7c} +\end{equation}\)

+
+
+
+ + + + +
+ +
+ + +
+

previous

+

wradlib data quality

+
+ + +
+

next

+

wradlib time series data and quasi vertical profiles

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/wradlib/wradlib_intro.html b/_preview/104/notebooks/wradlib/wradlib_intro.html new file mode 100644 index 00000000..6d5797ae --- /dev/null +++ b/_preview/104/notebooks/wradlib/wradlib_intro.html @@ -0,0 +1,599 @@ + + + + + + + + An Overview of wradlib — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

wradlib logo png

+
+

An Overview of wradlib

+
+
+

wradlib Introduction

+

\(\omega radlib\) was one of the first available free and open source Python packages which was targeting the whole processing chain of weather radar data.

+

From the beginning in 2011 it is available for collaboration in the cloud, first at bitbucket and from 2016 at it’s current location at github.

+

\(\omega radlib\) evolved constantly over time also adapting to new and emerging features of the Scientific Python stack. Many Features have been added ever since and \(\omega radlib\) is used in almost all parts of the world.

+

\(\omega radlib\)’s development paradigm Keep the magic to a minimum with a transparent, but lower level code is still a main goal of all development activities. Also the flat (or no) data model with passing data as numpy arrays and metadata as dictionaries is up to this time base for many functions in \(\omega radlib\). With the adoption of the emerging Xarray package this changed to some extent, combining data and metadata in a convenient way.

+

In this short course we will concentrate on:

+
    +

  1. reading, exploring and exporting radar data, gridding and gis export

    2. +

  2. data quality and beam blockage

    3. +

  3. processing of differential phase

    4. +

  4. quasi vertical profiles

    5. +
+

For a more comprehensive set of examples and tutorials please refer to the \(\omega radlib\) documentation.

+
+
+ + + + +
+ +
+ + +
+

previous

+

Wradlib Tutorial

+
+ + +
+

next

+

wradlib radar data io, visualisation, gridding and gis export

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/wradlib/wradlib_quasi_vertical_profiles.html b/_preview/104/notebooks/wradlib/wradlib_quasi_vertical_profiles.html new file mode 100644 index 00000000..878d774d --- /dev/null +++ b/_preview/104/notebooks/wradlib/wradlib_quasi_vertical_profiles.html @@ -0,0 +1,6624 @@ + + + + + + + + wradlib time series data and quasi vertical profiles — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

wradlib logo png

+
+

wradlib time series data and quasi vertical profiles

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. Reading radar sweep timeseries data into xarray based RadarVolume

    2. +

  2. Examination of RadarVolume and Sweep

    3. +

  3. Calculation of Quasivertical Profiles and Plotting

    4. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Matplotlib Basics

Helpful

Basic Plotting

Xarray Basics

Helpful

Basic Dataset/DataArray

Xarray Plotting

Helpful

Basic Plotting/Faceting

+
    +

  • Time to learn: 7.5 minutes

    • +
+
+
+
+

Imports

+
+
+
import glob
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from tqdm import tqdm_notebook as tqdm
+
+import wradlib as wrl
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

Import Australian Radar Data

+

It is assumed, that data from IDR71 (Terrey Hills, Sidney) from 20th of December 2018 is used in this notebook.

+
+
+
fglob = "data/hdf5/terrey_*.h5"
+idr71 = glob.glob(fglob)
+idr71.sort()
+print("Files available: {}".format(len(idr71)))
+
+
+
+
+
Files available: 40
+
+
+
+
+
+
+

Single Quasi Vertical Profile (QVP)

+
+
+
odh = wrl.io.open_odim_dataset(idr71[24])
+
+
+
+
+
+
+
display(odh)
+
+
+
+
+
<wradlib.RadarVolume>
+Dimension(s): (sweep: 1)
+Elevation(s): (32.0)
+
+
+
+
+

This example shows how to create a so called QVP. We need to define a function to add a height coordinate for plotting.

+
+
+
def add_height(ds):
+    ds = ds.pipe(wrl.georef.georeference_dataset)
+    height = ds.z.mean("azimuth")
+    ds = ds.assign_coords({"height": (["range"], height.data)})
+    return ds
+
+
+
+
+

Here we add the height coordinate and calculate the mean over the azimuth using the sweep with the highest available elevation.

+
+
+
swp = odh[0].pipe(add_height)
+display(swp)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 200)
+Coordinates: (12/16)
+  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float32 32.0 32.0 32.0 32.0 ... 32.0 32.0 32.0 32.0
+    rtime       (azimuth) datetime64[ns] 2018-12-20T07:28:41.598600960 ... 20...
+  * range       (range) float32 125.0 375.0 625.0 ... 4.962e+04 4.988e+04
+    time        datetime64[ns] 2018-12-20T07:28:37
+    sweep_mode  <U20 'azimuth_surveillance'
+    ...          ...
+    y           (azimuth, range) float32 106.0 318.0 ... 4.195e+04 4.216e+04
+    z           (azimuth, range) float32 262.0 395.0 ... 2.66e+04 2.673e+04
+    gr          (azimuth, range) float32 106.0 318.0 ... 4.195e+04 4.216e+04
+    rays        (azimuth, range) float32 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
+    bins        (azimuth, range) float32 125.0 375.0 ... 4.962e+04 4.988e+04
+    height      (range) float32 262.0 395.0 527.0 ... 2.66e+04 2.673e+04
+Data variables:
+    DBZH        (azimuth, range) float32 nan 0.5 7.5 3.0 ... nan nan nan nan
+    VRADH       (azimuth, range) float32 -25.99 -11.35 -4.951 ... nan nan nan
+    WRADH       (azimuth, range) float32 7.17 1.639 5.224 3.892 ... nan nan nan
+    TH          (azimuth, range) float32 15.5 1.0 7.5 3.0 ... nan nan nan nan
+    ZDR         (azimuth, range) float32 nan 1.606 3.102 1.134 ... nan nan nan
+    RHOHV       (azimuth, range) float32 nan 0.9803 0.7756 0.748 ... nan nan nan
+    PHIDP       (azimuth, range) float32 nan 121.9 103.5 121.9 ... nan nan nan
+    SNRH        (azimuth, range) float32 52.5 48.5 51.0 43.5 ... 0.5 0.0 1.5 1.0
+Attributes:
+    fixed_angle:  32.0
+
+
+
+
qvp = swp.mean("azimuth")
+qvp
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (range: 200)
+Coordinates:
+  * range       (range) float32 125.0 375.0 625.0 ... 4.962e+04 4.988e+04
+    time        datetime64[ns] 2018-12-20T07:28:37
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 151.2
+    latitude    float64 -33.7
+    altitude    float64 195.0
+    height      (range) float32 262.0 395.0 527.0 ... 2.66e+04 2.673e+04
+Data variables:
+    DBZH        (range) float32 1.143 -1.34 4.022 6.988 ... nan nan nan nan
+    VRADH       (range) float32 -3.843 -2.17 -3.047 -3.86 ... nan nan nan nan
+    WRADH       (range) float32 6.623 4.058 4.071 3.56 3.372 ... nan nan nan nan
+    TH          (range) float32 15.55 2.456 6.001 7.233 ... nan nan nan nan
+    ZDR         (range) float32 -2.409 2.278 1.928 1.512 ... nan nan nan nan
+    RHOHV       (range) float32 0.1409 0.6495 0.7601 0.8402 ... nan nan nan nan
+    PHIDP       (range) float32 256.0 104.2 112.2 115.2 ... nan nan nan nan
+    SNRH        (range) float32 55.48 46.51 47.43 47.48 ... 0.5639 0.5159 0.6967
+
+
+
+
qvp.DBZH.plot(y="height", figsize=(5, 10))
+
+
+
+
+
[<matplotlib.lines.Line2D at 0x7f2ff5c3e070>]
+
+
+../../_images/wradlib_quasi_vertical_profiles_18_1.png +
+
+
+
+

TimeSeries QVP

+

All wradlib xarray backends have the capability to read multiple sweeps/volumes in one go. We have to prepare the list of files a bit, though.

+
+
+
ts = xr.open_mfdataset(
+    idr71,
+    engine="odim",
+    group="dataset1",
+    combine="nested",
+    concat_dim="time",
+)
+
+
+
+
+
+
+
display(ts)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (time: 40, azimuth: 360, range: 200)
+Coordinates:
+  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    rtime       (time, azimuth) datetime64[ns] dask.array<chunksize=(1, 360), meta=np.ndarray>
+  * range       (range) float32 125.0 375.0 625.0 ... 4.962e+04 4.988e+04
+  * time        (time) datetime64[ns] 2018-12-20T05:04:32 ... 2018-12-20T08:5...
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 151.2
+    latitude    float64 -33.7
+    altitude    float64 195.0
+Data variables:
+    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    VRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    WRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    TH          (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    SNRH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+Attributes:
+    fixed_angle:  32.0
+
+
+

Georeference and add height coordinate

+
+
+
ts = ts.pipe(add_height)
+display(ts)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (time: 40, azimuth: 360, range: 200)
+Coordinates: (12/16)
+  * azimuth     (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
+    rtime       (time, azimuth) datetime64[ns] dask.array<chunksize=(1, 360), meta=np.ndarray>
+  * range       (range) float32 125.0 375.0 625.0 ... 4.962e+04 4.988e+04
+  * time        (time) datetime64[ns] 2018-12-20T05:04:32 ... 2018-12-20T08:5...
+    sweep_mode  <U20 'azimuth_surveillance'
+    ...          ...
+    y           (azimuth, range) float32 106.0 318.0 ... 4.195e+04 4.216e+04
+    z           (azimuth, range) float32 262.0 395.0 ... 2.66e+04 2.673e+04
+    gr          (azimuth, range) float32 106.0 318.0 ... 4.195e+04 4.216e+04
+    rays        (azimuth, range) float32 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
+    bins        (azimuth, range) float32 125.0 375.0 ... 4.962e+04 4.988e+04
+    height      (range) float32 262.0 395.0 527.0 ... 2.66e+04 2.673e+04
+Data variables:
+    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    VRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    WRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    TH          (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+    SNRH        (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 200), meta=np.ndarray>
+Attributes:
+    fixed_angle:  32.0
+
+
+
+

Calculate Statistics

+
+
+
stats = ["median", "mean", "min", "max"]
+stat = [
+    getattr(ts.where(ts.RHOHV > 0.8), st)("azimuth", skipna=True, keep_attrs=True)
+    for st in stats
+]
+ts_stats = xr.concat(stat, dim="stats")
+ts_stats = ts_stats.assign_coords({"stats": stats})
+
+
+
+
+
+
+
display(ts_stats)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (stats: 4, time: 40, range: 200)
+Coordinates:
+  * range       (range) float32 125.0 375.0 625.0 ... 4.962e+04 4.988e+04
+  * time        (time) datetime64[ns] 2018-12-20T05:04:32 ... 2018-12-20T08:5...
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 151.2
+    latitude    float64 -33.7
+    altitude    float64 195.0
+    height      (range) float32 262.0 395.0 527.0 ... 2.66e+04 2.673e+04
+  * stats       (stats) <U6 'median' 'mean' 'min' 'max'
+Data variables:
+    DBZH        (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    VRADH       (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    WRADH       (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    TH          (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    ZDR         (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    RHOHV       (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    PHIDP       (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+    SNRH        (stats, time, range) float32 dask.array<chunksize=(1, 1, 200), meta=np.ndarray>
+Attributes:
+    fixed_angle:  32.0
+
+
+
+

Plot QVP’s

+
+
+
levels = np.arange(-30, 80, 5)
+facet = ts_stats.TH.plot(
+    x="time",
+    y="height",
+    col="stats",
+    col_wrap=2,
+    cmap="turbo",
+    figsize=(12, 10),
+    levels=levels,
+)
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered
+  return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)
+/srv/conda/envs/notebook/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered
+  return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)
+/srv/conda/envs/notebook/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
+  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
+
+
+../../_images/wradlib_quasi_vertical_profiles_28_1.png +
+
+
+
+
+
+

Summary

+

Easy creation of Quasi Vertical Profiles was shown.

+
+
+

Resources and references

+ +
+
+ + + + +
+ +
+ + +
+

previous

+

wradlib Phase Processing - System PhiDP - ZPHI-Method

+
+ + +
+

next

+

BALTRAD Tutorial

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ Meteoswiss +
+
+ Meteofrance +
+
+ ARM +
+
+ Argonne +
+
+ Bonne +
+
+ Canada +
+
+ NCAR +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/notebooks/wradlib/wradlib_radar_data_io_vis.html b/_preview/104/notebooks/wradlib/wradlib_radar_data_io_vis.html new file mode 100644 index 00000000..b7faf6b8 --- /dev/null +++ b/_preview/104/notebooks/wradlib/wradlib_radar_data_io_vis.html @@ -0,0 +1,4742 @@ + + + + + + + + wradlib radar data io, visualisation, gridding and gis export — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

wradlib logo png

+
+

wradlib radar data io, visualisation, gridding and gis export

+
+
+

Overview

+

Within this notebook, we will cover:

+
    +

  1. Reading radar volume data into xarray based RadarVolume

    2. +

  2. Examination of RadarVolume and Sweeps

    3. +

  3. Plotting of sweeps, simple and mapmaking

    4. +

  4. Gridding and GIS output

    5. +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Xarray Basics

Helpful

Basic Dataset/DataArray

Matplotlib Basics

Helpful

Basic Plotting

Cartopy Basics

Helpful

Projections

GDAL Basiscs

Helpful

Raster

+
    +

  • Time to learn: 15 minutes

    • +
+
+
+
+

Imports

+
+
+
import glob
+import pathlib
+
+import cartopy
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from matplotlib import ticker as tick
+from osgeo import gdal
+
+import wradlib as wrl
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.8) or chardet (5.2.0)/charset_normalizer (2.0.10) doesn't match a supported version!
+  warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
+
+
+
+
+
+
+

Import data into RadarVolume

+

We have this special case here with Rainbow data where moments are splitted across files. Each file nevertheless consists of all sweeps comprising the volume. We’ll use some special nested ordering to read the files.

+
+
+
fglob = "data/rainbow/meteoswiss/*.vol"
+
+
+
+
+
+
+
vol = wrl.io.open_rainbow_mfdataset(fglob, combine="by_coords", concat_dim=None)
+
+
+
+
+
+
+
+

Examine RadarVolume

+

The RadarVolume is a shallow class which tries to comply to CfRadial2/WMO-FM301, see WMO-CF_Extensions.

+

The printout of RadarVolume just lists the dimensions and the associated elevations.

+
+
+
display(vol)
+
+
+
+
+
<wradlib.RadarVolume>
+Dimension(s): (sweep: 10)
+Elevation(s): (0.0, 1.3, 2.9, 4.9, 7.3, 10.2, 13.8, 18.2, 23.5, 30.0)
+
+
+
+
+
+
+

Root Group

+

The root-group is essentially an overview over the volume, more or less aligned with CfRadial metadata.

+
+
+
vol.root
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:              (sweep: 10)
+Coordinates:
+    time                 datetime64[ns] 2019-10-21T08:24:09
+    longitude            float64 6.954
+    altitude             float64 735.0
+    sweep_mode           <U20 'azimuth_surveillance'
+    latitude             float64 46.77
+Dimensions without coordinates: sweep
+Data variables:
+    volume_number        int64 0
+    platform_type        <U5 'fixed'
+    instrument_type      <U5 'radar'
+    primary_axis         <U6 'axis_z'
+    time_coverage_start  <U20 '2019-10-21T08:24:09Z'
+    time_coverage_end    <U20 '2019-10-21T08:29:33Z'
+    sweep_group_name     (sweep) <U7 'sweep_0' 'sweep_1' ... 'sweep_8' 'sweep_9'
+    sweep_fixed_angle    (sweep) float64 0.0 1.3 2.9 4.9 ... 13.8 18.2 23.5 30.0
+Attributes:
+    version:          None
+    title:            None
+    institution:      None
+    references:       None
+    source:           None
+    history:          None
+    comment:          im/exported using wradlib
+    instrument_name:  None
+    fixed_angle:      0.0
+
+
+
+

Sweep Groups

+

Sweeps are available in a sequence attached to the RadarVolume object.

+
+
+
swp = vol[0]
+display(swp)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 1400)
+Coordinates:
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+  * range       (range) float32 25.0 75.0 125.0 ... 6.992e+04 6.998e+04
+    elevation   (azimuth) float64 dask.array<chunksize=(360,), meta=np.ndarray>
+    time        datetime64[ns] 2019-10-21T08:24:09
+    rtime       (azimuth) datetime64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+    longitude   float64 6.954
+    latitude    float64 46.77
+    altitude    float64 735.0
+    sweep_mode  <U20 'azimuth_surveillance'
+Data variables:
+    DBZH        (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    KDP         (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    PHIDP       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    RHOHV       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    VRADH       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    WRADH       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    ZDR         (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+Attributes:
+    fixed_angle:  0.0
+
+
+
+
+

Inspect Scan Strategy

+

Considering volume files it’s nice to have an overview over the scan strategy. We can choose some reasonable values for the layout.

+
+
+
nrays = 360
+nbins = 150
+range_res = 1000.0
+ranges = np.arange(nbins) * range_res
+elevs = vol.root.sweep_fixed_angle.values
+sitecoords = (
+    vol.root.longitude.values.item(),
+    vol.root.latitude.values.item(),
+    vol.root.altitude.values.item(),
+)
+
+beamwidth = 1.0
+
+
+
+
+
+
+
ax = wrl.vis.plot_scan_strategy(ranges, elevs, sitecoords)
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_19_0.png +
+
+

We can plot it on top of the terrain derived from SRTM DEM.

+
+
+
import os
+
+os.environ["WRADLIB_EARTHDATA_BEARER_TOKEN"] = ""
+os.environ["WRADLIB_DATA"] = "data/wradlib-data"
+
+
+
+
+
+
+
ax = wrl.vis.plot_scan_strategy(ranges, elevs, sitecoords, terrain=True)
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_22_0.png +
+
+

Let’s make the earth go round…

+
+
+
ax = wrl.vis.plot_scan_strategy(
+    ranges, elevs, sitecoords, cg=True, terrain=True, az=180
+)
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_24_0.png +
+
+
+
+

Plotting Radar Data

+
+

Time vs. Azimuth

+
+
+
fig = plt.figure(figsize=(10, 5))
+ax1 = fig.add_subplot(111)
+swp.azimuth.sortby("rtime").plot(x="rtime", marker=".")
+
+
+
+
+
[<matplotlib.lines.Line2D at 0x7eff14b18460>]
+
+
+../../_images/wradlib_radar_data_io_vis_26_1.png +
+
+
+
+

Range vs. Azimuth/Time

+
+
+
fig = plt.figure(figsize=(10, 5))
+ax1 = fig.add_subplot(121)
+swp.DBZH.plot(cmap="turbo", ax=ax1)
+ax1.set_title(f"{swp.time.values.astype('M8[s]')}")
+ax2 = fig.add_subplot(122)
+swp.DBZH.sortby("rtime").plot(y="rtime", cmap="turbo", ax=ax2)
+ax2.set_title(f"{swp.time.values.astype('M8[s]')}")
+plt.tight_layout()
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_28_0.png +
+
+
+
+

Georeferenced as Plan Position Indicator

+
+
+
fig = plt.figure(figsize=(10, 10))
+ax1 = fig.add_subplot(111)
+swp.DBZH.pipe(wrl.georef.georeference_dataset).plot(
+    x="x", y="y", ax=ax1, cmap="turbo", cbar_kwargs=dict(shrink=0.8)
+)
+ax1.plot(0, 0, "rx", markersize=12)
+ax1.set_title(f"{swp.time.values.astype('M8[s]')}")
+ax1.grid()
+ax1.set_aspect("equal")
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_30_0.png +
+
+
+
+

Basic MapMaking with cartopy

+

The data will be georeferenced as Azimuthal Equidistant Projection centered at the radar. For the map projection we will use Mercator.

+
+
+
map_trans = ccrs.AzimuthalEquidistant(
+    central_latitude=swp.latitude.values, central_longitude=swp.longitude.values
+)
+map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
+
+
+
+
+
+
+
def plot_borders(ax):
+    borders = cfeature.NaturalEarthFeature(
+        category="cultural", name="admin_0_countries", scale="10m", facecolor="none"
+    )
+    ax.add_feature(borders, edgecolor="black", lw=2, zorder=4)
+
+
+
+
+
+
+
fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection=map_proj)
+cbar_kwargs = dict(shrink=0.7, pad=0.075)
+pm = swp.DBZH.pipe(wrl.georef.georeference_dataset).plot(
+    ax=ax, x="x", y="y", cbar_kwargs=cbar_kwargs, cmap="turbo", transform=map_trans
+)
+plot_borders(ax)
+ax.gridlines(draw_labels=True)
+ax.plot(
+    swp.longitude.values, swp.latitude.values, transform=map_trans, marker="*", c="r"
+)
+ax.set_title(f"{swp.time.values.astype('M8[s]')}")
+ax.set_xlim(-15e4, 45e4)
+ax.set_ylim(565e4, 610e4)
+plt.tight_layout()
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_34_0.png +
+
+
+
+

Plot on curvelinear grid

+

For Xarray DataArrays wradlib uses a so-called accessor (wradlib). To plot on curvelinear grids projection has to be set to cg, which uses the matplotlib AXISARTIS namespace.

+
+
+
fig = plt.figure(figsize=(10, 8))
+
+pm = swp.DBZH.pipe(wrl.georef.georeference_dataset).wradlib.plot(
+    proj="cg", fig=fig, cmap="turbo"
+)
+
+ax = plt.gca()
+
+# apply eye-candy
+caax = ax.parasites[0]
+paax = ax.parasites[1]
+ax.parasites[1].set_aspect("equal")
+t = plt.title(f"{vol[0].time.values.astype('M8[s]')}", y=1.05)
+cbar = plt.colorbar(pm, pad=0.075, ax=paax)
+caax.set_xlabel("x_range [m]")
+caax.set_ylabel("y_range [m]")
+plt.text(1.0, 1.05, "azimuth", transform=caax.transAxes, va="bottom", ha="right")
+cbar.set_label("reflectivity [dBZ]")
+
+
+
+
+../../_images/wradlib_radar_data_io_vis_36_0.png +
+
+
+
+
+

ODIM_H5 format export and import

+
+

Export to ODIM_H5

+
+
+
vol.to_odim("test_odim_vol.h5")
+
+
+
+
+
+
+

Import from ODIM_H5

+
+
+
vol2 = wrl.io.open_odim_dataset("test_odim_vol.h5")
+display(vol2)
+
+
+
+
+
<wradlib.RadarVolume>
+Dimension(s): (sweep: 10)
+Elevation(s): (0.0, 1.3, 2.9, 4.9, 7.3, 10.2, 13.8, 18.2, 23.5, 30.0)
+
+
+
+
+
+
+
display(vol2[0])
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 1400)
+Coordinates:
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
+    rtime       (azimuth) datetime64[ns] 2019-10-21T08:24:27.875000064 ... 20...
+  * range       (range) float32 25.0 75.0 125.0 ... 6.992e+04 6.998e+04
+    time        datetime64[ns] 2019-10-21T08:24:09
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 6.954
+    latitude    float64 46.77
+    altitude    float64 735.0
+Data variables:
+    DBZH        (azimuth, range) float32 ...
+    KDP         (azimuth, range) float32 ...
+    PHIDP       (azimuth, range) float32 ...
+    RHOHV       (azimuth, range) float32 ...
+    VRADH       (azimuth, range) float32 ...
+    WRADH       (azimuth, range) float32 ...
+    ZDR         (azimuth, range) float32 ...
+Attributes:
+    fixed_angle:  0.0
+
+
+
+
+

Import with xarray backends

+

We can facilitate the xarray backend’s which wradlib provides for the different readers. The xarray backends are capable of loading data into a single Dataset for now. So we need to give some information here too.

+
+

Open single files

+

The simplest case can only open one file and one group a time!

+
+
+
ds = xr.open_dataset("test_odim_vol.h5", engine="odim", group="dataset1")
+display(ds)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 1400)
+Coordinates:
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+    elevation   (azimuth) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
+    rtime       (azimuth) datetime64[ns] 2019-10-21T08:24:27.875000064 ... 20...
+  * range       (range) float32 25.0 75.0 125.0 ... 6.992e+04 6.998e+04
+    time        datetime64[ns] 2019-10-21T08:24:09
+    sweep_mode  <U20 'azimuth_surveillance'
+    longitude   float64 6.954
+    latitude    float64 46.77
+    altitude    float64 735.0
+Data variables:
+    DBZH        (azimuth, range) float32 ...
+    KDP         (azimuth, range) float32 ...
+    PHIDP       (azimuth, range) float32 ...
+    RHOHV       (azimuth, range) float32 ...
+    VRADH       (azimuth, range) float32 ...
+    WRADH       (azimuth, range) float32 ...
+    ZDR         (azimuth, range) float32 ...
+Attributes:
+    fixed_angle:  0.0
+
+
+
+

Open multiple files

+

Here we just specify the group, which in case of rainbow files is given by the group number.

+
+
+
ds = xr.open_mfdataset(fglob, engine="rainbow", group=0, combine="by_coords")
+display(ds)
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:     (azimuth: 360, range: 1400)
+Coordinates:
+  * azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
+  * range       (range) float32 25.0 75.0 125.0 ... 6.992e+04 6.998e+04
+    elevation   (azimuth) float64 dask.array<chunksize=(360,), meta=np.ndarray>
+    time        datetime64[ns] 2019-10-21T08:24:09
+    rtime       (azimuth) datetime64[ns] dask.array<chunksize=(360,), meta=np.ndarray>
+    longitude   float64 6.954
+    latitude    float64 46.77
+    altitude    float64 735.0
+    sweep_mode  <U20 'azimuth_surveillance'
+Data variables:
+    DBZH        (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    KDP         (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    PHIDP       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    RHOHV       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    VRADH       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    WRADH       (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+    ZDR         (azimuth, range) float32 dask.array<chunksize=(360, 1400), meta=np.ndarray>
+Attributes:
+    fixed_angle:  0.0
+
+
+
+
+

Gridding and Export to GIS formats

+
    +

  • get coordinates from source Dataset with given projection

    • +

  • calculate target coordinates

    • +

  • grid using wradlib interpolator

    • +

  • export to single band geotiff

    • +

  • use GDAL CLI tools to convert to grayscaled/paletted PNG

    • +
+
+
+
def get_target_grid(ds, nb_pixels):
+    xgrid = np.linspace(ds.x.min(), ds.x.max(), nb_pixels, dtype=np.float32)
+    ygrid = np.linspace(ds.y.min(), ds.y.max(), nb_pixels, dtype=np.float32)
+    grid_xy_raw = np.meshgrid(xgrid, ygrid)
+    grid_xy_grid = np.dstack((grid_xy_raw[0], grid_xy_raw[1]))
+    return xgrid, ygrid, grid_xy_grid
+
+
+def get_target_coordinates(grid):
+    grid_xy = np.stack((grid[..., 0].ravel(), grid[..., 1].ravel()), axis=-1)
+    return grid_xy
+
+
+def get_source_coordinates(ds):
+    xy = np.stack((ds.x.values.ravel(), ds.y.values.ravel()), axis=-1)
+    return xy
+
+
+def coordinates(da, proj, res=100):
+    # georeference single sweep
+    da = da.pipe(wrl.georef.georeference_dataset, proj=proj)
+    # get source coordinates
+    src = get_source_coordinates(da)
+    # create target grid
+    xgrid, ygrid, trg = get_target_grid(da, res)
+    return src, trg
+
+
+def moment_to_gdal(da, trg_grid, driver, ext, path="", proj=None):
+    # use wgs84 pseudo mercator if no projection is given
+    if proj is None:
+        proj = wrl.georef.epsg_to_osr(3857)
+    t = da.time.values.astype("M8[s]").astype("O")
+    outfilename = f"gridded_{da.name}_{t:%Y%m%d}_{t:%H%M%S}"
+    outfilename = os.path.join(path, outfilename)
+    f = pathlib.Path(outfilename)
+    f.unlink(missing_ok=True)
+    res = ip_near(da.values.ravel(), maxdist=1000).reshape(
+        (len(trg_grid[0]), len(trg_grid[1]))
+    )
+    data, xy = wrl.georef.set_raster_origin(res, trg_grid, "upper")
+    ds = wrl.georef.create_raster_dataset(data, xy, projection=proj)
+    wrl.io.write_raster_dataset(outfilename + ext, ds, driver)
+
+
+
+
+
+

Coordinates

+
+
+
%%time
+epsg_code = 2056
+proj = wrl.georef.epsg_to_osr(epsg_code)
+src, trg = coordinates(ds, proj, res=1400)
+
+
+
+
+
CPU times: user 867 ms, sys: 56.2 ms, total: 924 ms
+Wall time: 923 ms
+
+
+
+
+
+
+

Interpolator

+
+
+
%%time
+ip_near = wrl.ipol.Nearest(src, trg.reshape(-1, trg.shape[-1]), remove_missing=7)
+
+
+
+
+
CPU times: user 2.66 s, sys: 15.7 ms, total: 2.68 s
+Wall time: 2.68 s
+
+
+
+
+
+
+

Gridding and Export

+
+
+
%%time
+moment_to_gdal(ds.DBZH, trg, "GTiff", ".tif", proj=proj)
+
+
+
+
+
CPU times: user 164 ms, sys: 12 ms, total: 176 ms
+Wall time: 176 ms
+
+
+
+
+
+
+

GDAL info on created GeoTiff

+
+
+
!gdalinfo gridded_DBZH_20191021_082409.tif
+
+
+
+
+
Driver: GTiff/GeoTIFF
+Files: gridded_DBZH_20191021_082409.tif
+Size is 1400, 1400
+
+
+
Coordinate System is:
+PROJCRS["CH1903+ / LV95",
+    BASEGEOGCRS["CH1903+",
+        DATUM["CH1903+",
+            ELLIPSOID["Bessel 1841",6377397.155,299.1528128,
+                LENGTHUNIT["metre",1]]],
+        PRIMEM["Greenwich",0,
+            ANGLEUNIT["degree",0.0174532925199433]],
+        ID["EPSG",4150]],
+    CONVERSION["Swiss Oblique Mercator 1995",
+        METHOD["Hotine Oblique Mercator (variant B)",
+            ID["EPSG",9815]],
+        PARAMETER["Latitude of projection centre",46.9524055555556,
+            ANGLEUNIT["degree",0.0174532925199433],
+            ID["EPSG",8811]],
+        PARAMETER["Longitude of projection centre",7.43958333333333,
+            ANGLEUNIT["degree",0.0174532925199433],
+            ID["EPSG",8812]],
+        PARAMETER["Azimuth of initial line",90,
+            ANGLEUNIT["degree",0.0174532925199433],
+            ID["EPSG",8813]],
+        PARAMETER["Angle from Rectified to Skew Grid",90,
+            ANGLEUNIT["degree",0.0174532925199433],
+            ID["EPSG",8814]],
+        PARAMETER["Scale factor on initial line",1,
+            SCALEUNIT["unity",1],
+            ID["EPSG",8815]],
+        PARAMETER["Easting at projection centre",2600000,
+            LENGTHUNIT["metre",1],
+            ID["EPSG",8816]],
+        PARAMETER["Northing at projection centre",1200000,
+            LENGTHUNIT["metre",1],
+            ID["EPSG",8817]]],
+    CS[Cartesian,2],
+        AXIS["(E)",east,
+            ORDER[1],
+            LENGTHUNIT["metre",1]],
+        AXIS["(N)",north,
+            ORDER[2],
+            LENGTHUNIT["metre",1]],
+    USAGE[
+        SCOPE["Cadastre, engineering survey, topographic mapping (large and medium scale)."],
+        AREA["Liechtenstein; Switzerland."],
+        BBOX[45.82,5.96,47.81,10.49]],
+    ID["EPSG",2056]]
+Data axis to CRS axis mapping: 1,2
+Origin = (2492961.000000000000000,1249970.687500000000000)
+Pixel Size = (100.000000000000000,-100.125000000000000)
+Metadata:
+  AREA_OR_POINT=Area
+Image Structure Metadata:
+  INTERLEAVE=BAND
+Corner Coordinates:
+Upper Left  ( 2492961.000, 1249970.688) (  6d 1'17.65"E, 47d23'35.61"N)
+Lower Left  ( 2492961.000, 1109795.688) (  6d 3'15.75"E, 46d 7'56.52"N)
+Upper Right ( 2632961.000, 1249970.688) (  7d52'34.61"E, 47d24' 3.98"N)
+Lower Right ( 2632961.000, 1109795.688) (  7d51'58.24"E, 46d 8'24.24"N)
+Center      ( 2562961.000, 1179883.188) (  6d57'16.56"E, 46d46'13.43"N)
+Band 1 Block=1400x1 Type=Float32, ColorInterp=Gray
+  NoData Value=-9999
+
+
+
+
+
+
+

Translate exported GeoTiff to grayscale PNG

+
+
+
!gdal_translate -of PNG -ot Byte -scale -30. 60. 0 255 gridded_DBZH_20191021_082409.tif grayscale.png
+
+
+
+
+
Input file size is 1400, 1400
+Warning 1: for band 1, nodata value has been clamped to 0, the original value being out of range.
+
+
+
0...10...20...30...40...50...60...70...80...90..
+
+
+
.100 - done.
+
+
+
+
+
+
+

Apply colortable to PNG

+
+
+
with open("colors.txt", "w") as f:
+    f.write("0 blue\n")
+    f.write("50 yellow\n")
+    f.write("100 yellow\n")
+    f.write("150 orange\n")
+    f.write("200 red\n")
+    f.write("250 white\n")
+
+
+
+
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+

Display exported PNG’s

+
+
+
!gdaldem color-relief grayscale.png colors.txt paletted.png
+
+
+
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+
0...
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+
10...20...30...40...
+
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+
50...60...70...80...90...100 - done.
+
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+
+
+

grayscale png +paletted png

+
+
+

Import with Xarray, rasterio backend

+
+
+
with xr.open_dataset("gridded_DBZH_20191021_082409.tif", engine="rasterio") as ds_grd:
+    display(ds_grd)
+    ds_grd.band_data.plot(cmap="turbo")
+
+
+
+
+
/srv/conda/envs/notebook/lib/python3.9/site-packages/pyproj/crs/_cf1x8.py:511: UserWarning: angle from rectified to skew grid parameter lost in conversion to CF
+  warnings.warn(
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:      (band: 1, x: 1400, y: 1400)
+Coordinates:
+  * band         (band) int64 1
+  * x            (x) float64 2.493e+06 2.493e+06 ... 2.633e+06 2.633e+06
+  * y            (y) float64 1.25e+06 1.25e+06 1.25e+06 ... 1.11e+06 1.11e+06
+    spatial_ref  int64 0
+Data variables:
+    band_data    (band, y, x) float32 ...
../../_images/wradlib_radar_data_io_vis_64_2.png +
+
+
+
+
+
+

Summary

+

We’ve just learned how to use \(\omega radlib\)’s xarray backends to make radar volume data available as xarray Datasets and DataArrays. Accessing, plotting and exporting data has been shown.

+
+

What’s next?

+

In the next notebook we dive into data quality processing.

+
+
+ +
+ + + + + + + + +
+ +
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+

previous

+

An Overview of wradlib

+
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next

+

wradlib data quality

+
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+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

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+ + \ No newline at end of file diff --git a/_preview/104/objects.inv b/_preview/104/objects.inv new file mode 100644 index 00000000..8eed2ee5 Binary files /dev/null and b/_preview/104/objects.inv differ diff --git a/_preview/104/package-development/README.html b/_preview/104/package-development/README.html new file mode 100644 index 00000000..cb7481de --- /dev/null +++ b/_preview/104/package-development/README.html @@ -0,0 +1,634 @@ + + + + + + + + Developing Open Source Software Packages — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
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+
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+ +
+ +
+

Developing Open Source Software Packages

+

This section covers best practises of contributing to existing software packages +and of creating new packages.

+
+

HowTo collaborate

+

Collaboration is a wide field, not only code contribution.

+
    +

  • GitHub Discussions

    + +
    • +

  • Issues

    + +
    • +

  • Pull Request/Merge Request

    +

    Not only code but also documentation can be added or extended. Even correcting typos is useful and appreciated by most package maintainers. +Sometimes issues are labeled (eg easy first issue) for easy identification.

    + +
    • +

  • Mailing Lists

    + +
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  • Discourse

    + +
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+

The main documentation of the projects are a good place to get information about collaboration. +For the projects involved here, those documentation locations can be found here:

+

https://openradarscience.org/projects/

+
+

Important

+

It’s good practice to adhere to the project’s Code of Conduct. But in any case - be friendly and welcoming.

+
+
+
+

HowTo create an own package

+
+

Important

+

Advise: use a cookiecutter! +A cookiecutter is a command-line utility that creates projects from cookiecutters (project templates), e.g. creating a Python package project from a Python package project template.

+
+

We use the Python cookiecutter-template from https://github.com/audreyr/cookiecutter-pypackage.git but without all bells and whistles.

+

Let’s just create a package with some function, install and test it.

+
+
+ + + + +
+ +
+ + +
+

previous

+

Doppler Velocity Dealiasing with Py-ART and BALTRAD

+
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+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
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+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/package-overview/README.html b/_preview/104/package-overview/README.html new file mode 100644 index 00000000..ae670a16 --- /dev/null +++ b/_preview/104/package-overview/README.html @@ -0,0 +1,552 @@ + + + + + + + + Open Radar Community — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Open Radar Community

+

An overview of the open radar science community.

+
+ + + + +
+ +
+
+
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+
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+
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+
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+ +
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+
+

+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/104/pyart/README.html b/_preview/104/pyart/README.html new file mode 100644 index 00000000..f2018816 --- /dev/null +++ b/_preview/104/pyart/README.html @@ -0,0 +1,606 @@ + + + + + + + + Py-ART Tutorial — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ + + + + + + +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +
+

Py-ART Tutorial

+

This is an overview of Py-ART, in 45 minutes!

+
+

Py-ART Basics and Gridding

+

The foundational content includes the:

+
    +

  • Py-ART Basics - an overview of Py-ART package, how to read in data, and basic plotting functionality

    • +

  • Py-ART Gridding - how to utilize the gridding tools in Py-ART

    • +
+

If you are new to Py-ART, starting with the basics is a good place to start, and is required to know before moving onto Py-ART Gridding.

+
+
+

Py-ART Corrections and Calculations

+

Once learning the basics, we jump into applying filtering, corrections, and analyzing wind data, focusing on:

+
    +

  • Filtering and retrievals on raw Swiss C-band data

    • +

  • Processing of Doppler wind data from a Swiss volumetric scan

    • +
+

These notebooks also come with an exercise, where you can apply the lessons learned from the basics and workflows to complete a set of tasks!

+
+
+
+
+ + + + +
+ +
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previous

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Open Radar Community

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+

Py-ART Basics

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rr:26,zhraw:26,zict:14,zip:[2,14,18,19],zipp:14,zlevel:19,zlib:[10,14],zmax:[17,19],zmin:[17,19],zonalstat:25,zorder:[7,15,25,29],zr:15,zstd:14,zunit:21},titles:["ERAD 2022 Open Radar Science Shortcourse","BALTRAD Tutorial","Getting Started","Open Radar Community","An Overview of Open Science","LROSE Tutorial","baltrad2wradlib","Interaction of BALTRAD and wradlib via ODIM_H5","Compositing with BALTRAD","In this notebook, we will use the depolarization ratio to quality control a volume of data from the new radar at Radisson, Saskatchewan","BALTRAD I/O model - making sense out of data and metadata","BALTRAD Quality Control","BALTRAD parallel processing","baltrad_short_course","Environment overview","ERAD 2022 Open Source Workshop","Project Pythia Notebook Template","Exercice Sample Solution","Filtering and retrievals on raw Swiss C-band data","Processing of Doppler wind data from a Swiss volumetric scan","Py-ART Basics","Py-ART Gridding","Exercice","In this notebook, an ODIM_H5 file is read using BALTRAD. Then the rain rate is determined from the calculated specific attenuation using Py-ART.","Doppler Velocity Dealiasing with Py-ART and BALTRAD","wradlib data quality","wradlib Phase Processing - System PhiDP - ZPHI-Method","An Overview of wradlib","wradlib time series data and quasi vertical profiles","wradlib radar data io, visualisation, gridding and gis export","Developing Open Source Software Packages","Open Radar Community","Py-ART Tutorial","Combining Radar Workflows","Wradlib Tutorial"],titleterms:{"07":23,"08":23,"1":2,"10":9,"1002":9,"1929":9,"2":2,"20":23,"2013":23,"2022":[0,15],"3":[2,15],"30":23,"8":23,"8080":23,"case":23,"do":[9,20],"export":[29,34],"import":[10,11,16,20,21,25,26,28,29],"new":[9,10,11],"short":34,"try":7,"while":10,A:[9,10,16],AND:9,In:[9,23],It:10,ON:23,TO:9,The:23,Then:23,a_:26,a_h:26,about:2,access:[10,11],account:2,add:[8,10,28],after:7,against:7,algorithm:11,all:[7,10],along:[10,11],alpha:26,also:[9,10],an:[4,10,11,20,21,23,27,30],angl:10,anoth:16,antenna:21,anyth:23,appli:[26,29],apply_ufunc:25,ar:10,art:[20,21,23,24,32],ascend:10,asynchron:12,attenu:[7,18,23,26,34],attribut:10,australian:28,avail:9,azimuth:[19,29],b:[10,11],back:[11,23],backend:29,baltrad2wradlib:6,baltrad:[1,7,8,10,11,12,23,24],baltrad_short_cours:13,band:18,base:[15,25],basic:[20,29,32],beam:11,beamb:11,beamblockag:[25,34],been:11,befor:7,being:10,binder:2,block:9,blockag:11,bogu:10,box:23,boxpol:26,bropo:11,browser:23,build:2,c:18,cal:26,calcul:[23,25,26,28,32],calendar:23,can:[10,20],canada:23,cappi:[9,19],cartesian:[15,21,23],cartopi:29,cfradial1:25,cfradial:15,chain:[8,11],characterist:10,check:[2,11],citi:23,classif:18,cloud:2,clutter:[18,25],cluttermap:25,coeffici:9,collabor:30,collect:7,colormap:9,colort:29,colour:9,column:15,combin:[15,33],command:23,commun:[3,31],compar:7,comparison:11,composit:8,compress:10,comput:15,conclus:2,configur:21,connect:23,constant:[10,11],contain:[10,11],content:16,contributor:0,control:[9,11,23],convert:[7,15,25],coordin:[21,25,28,29],core:12,correct:[7,9,18,23,26,32,34],correl:9,correspondong:8,cours:0,cpu:12,creat:[10,11,15,26,29,30],creation:10,criterion:8,cross:[9,15],cumul:25,curvelinear:[25,29],custom:2,d:15,danger:16,data:[7,9,10,11,15,18,19,20,21,23,25,26,28,29,34],dataset:[7,21],date:23,dbzh:11,dealia:11,dealias:[11,19,24],defin:12,delta:26,dem:25,demonstr:16,depolar:9,descend:10,design:7,detect:25,determin:23,develop:30,differenti:9,dimens:10,displai:[9,19,29],dispos:12,distribut:26,doe:10,doi:9,domain:26,don:10,done:23,doppler:[9,19,24],download:[15,25],dp:26,dropdown:23,drqc:9,e:15,earlier:8,ecco:15,echo:[11,15],ed:9,elabor:11,empti:10,environ:[2,7,14],erad:[0,15],estim:18,estonia:7,examin:29,exampl:[0,11,15,26],except:23,exercic:[17,22],exercis:[7,8,11],explicitli:10,explor:23,extract:20,f:26,faster:10,featur:34,feed:[12,23],field:[7,11,15,20,23],figur:7,file:[10,11,15,23,29],filter:[7,18],fire:23,first:[11,16],flood:23,format:[15,29],foundat:0,framework:23,from:[7,8,9,11,12,15,19,23,25,26,29],further:16,gabella:25,gdal:29,gear:11,gener:[9,12,23],georefer:28,georeferenc:[7,29],geotiff:29,get:2,gi:[29,34],github:2,go:[9,23],goodland:15,googlemapsplugin:23,grayscal:29,grid:[15,21,25,29,32,34],grid_from_radar:21,ground:18,group:29,h:26,handl:10,have:[2,10],hdf5:10,header:16,health:2,height:28,histori:20,horizont:[10,21],how:10,howto:30,http:[9,23],hvplot:25,hydrometeor:18,i:[10,11],icon:23,identifi:11,imag:23,incomplet:10,index:[8,25],indic:29,info:[16,29],inform:7,initi:15,input:[10,11],inspect:[7,29],instructor:0,integr:26,interact:[7,25],intern:10,internet:23,interpol:29,introduct:27,investig:20,io:[20,29,34],juli:23,k_:26,kdp:18,kgld:15,king:23,last:16,latitudin:21,launch:2,leav:9,legaci:9,level:16,line:23,list:[0,15],load:[9,23],loader:9,local:2,localhost:23,locat:15,log:2,look:9,lowest:[8,9,10,11],lrose:[5,15],main:[10,11],make:[2,10],manadatori:10,manag:10,mang:11,mani:10,map:[15,21,25],mapmak:29,mask:26,materi:9,max:15,maximum:8,mdvmerge2:15,merg:15,met:9,metadata:10,method:[26,34],microsoft:23,model:[10,15],modul:[10,11],moment:7,monster:12,mosaic:15,motiv:0,multipl:29,multiprocess:12,must:10,n:15,need:[0,25],nevertheless:10,nexrad:15,next:[16,20,21,25,26,29],nois:18,non:11,normal:23,note:[10,15],notebook:[2,9,16,23],now:[8,9,10],number:12,o:[10,11],object:[10,20,21],odc_toolbox:7,odim_h5:[7,10,11,23,29],offset:26,one:[7,15],ontario:23,open:[0,3,4,15,29,30,31],openli:9,option:10,org:9,origin:[7,9],our:[2,12,20,21],out:10,over:34,overshoot:11,overview:[4,14,16,20,21,25,26,27,28,29,34],own:30,packag:[11,30],pan:25,panel:23,pangeo:2,parallel:12,paramet:[10,15],partial:25,pass:10,payload:10,pgf:23,phase:26,phi_:26,phidp:26,pid:15,pixel:8,plan:[15,29],plenti:10,plot:[7,8,10,11,15,19,20,21,25,26,28,29],plotter:[8,11],plugin:11,png:29,polar:[9,11,15,26],polarimetr:7,popul:10,posit:29,ppi:[7,9],pre:[23,26],precipit:11,prefer:23,prepar:[0,7,9],prerequisit:[16,20,21,25,26,28,29],primit:10,probabl:11,process:[12,19,25,26,34],product:23,profil:[15,28,34],program:0,project:16,properti:10,provid:15,publish:[9,23],pullrequest:2,push:2,py:[20,21,23,24,32],pyart:[20,21],pythia:16,python:[10,12,15],qc:[7,8,9,11],qpe:18,qualiti:[8,9,11,25,34],quasi:[28,34],queri:10,quick:16,qvp:[28,34],r:26,radar:[0,3,9,15,20,23,25,28,29,31,33,34],radarmapdisplai:20,radarvolum:29,radial:9,radian:10,radisson:9,radx2grid:15,radxconvert:15,radxrat:15,rain:23,rang:29,rasterio:29,rate:[15,23],ratio:9,rave:[10,11,23],raw:[15,18],re:9,read:[7,9,10,11,15,18,19,20,21,23],reason:26,refer:[16,20,21,25,26,28,29],reflect:[8,9,10,11,15,23,25,26],remov:[10,11,18],replot:11,repres:10,requir:7,resourc:[16,20,21,25,26,28,29],result:[7,9,11,15],retriev:[18,23,26],risk:10,root:29,ropo:9,routin:21,rpc:23,ruc:15,rudimentari:8,run:[2,7,15],s:[7,10,11,15,16,20,21,23,25,26,28,29],sampl:[15,17,20],saskatchewan:9,save:[9,15],scan:[10,11,19,29],scienc:[0,4,9],second:16,section:[15,16],see:10,select:[7,15,23],sens:10,sequenc:23,sequenti:12,seri:[23,28],server:23,set:[9,10,15],setup:21,sever:23,shift:11,shortcours:0,should:23,showcas:26,simpl:11,singl:[28,29],site:10,slice:21,small:23,so:10,softwar:30,solut:17,some:[7,9,10],sound:15,sourc:[15,30],spdb:15,specif:23,spheric:25,srtm:25,start:[2,9],statist:28,stop:23,strategi:[10,29],string:23,structur:0,subsect:16,subsequ:9,success:16,suergaver:7,sum:26,summari:[16,20,21,25,26,28,29],sure:2,sweep:29,swiss:[18,19,25],system:26,t:10,tabl:9,target:7,temperatur:15,templat:16,termin:9,test:21,thei:10,thi:[7,9,10,23],thing:0,through:10,time:[15,23,28,29],timeseri:28,tool:0,topograph:11,toronto:23,total:[8,11],translat:29,tune:10,tutori:[1,5,32,34],tweak:8,type:15,under:23,unit:7,up:[9,21,23],us:[2,7,8,9,10,11,15,20,21,23,25,34],util:20,vad:19,veloc:[9,19,24],verifi:12,vertic:[15,28,34],via:7,view:[15,21,23],visual:[9,10,21],visualis:[29,34],volum:[9,10,11,25],volumetr:19,vrad:11,vs:[21,26,29],w:15,wa:9,warn:16,we:[7,9],web:15,were:10,what:[10,16,20,21,25,26,29],where:10,whether:11,whole:26,why:21,wind:[9,19],workflow:[0,2,15,33],workshop:15,wradlib:[7,25,26,27,28,29,34],wrap:25,write:10,xarrai:[21,25,29],xml:23,you:[0,2,10],your:[7,16,23],zoom:25,zphi:[26,34]}}) \ No newline at end of file diff --git a/_preview/104/workflows/README.html b/_preview/104/workflows/README.html new file mode 100644 index 00000000..9cbc0d5b --- /dev/null +++ b/_preview/104/workflows/README.html @@ -0,0 +1,570 @@ + + + + + + + + Combining Radar Workflows — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Combining Radar Workflows

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How to combine the different tools to complete your radar data workflow.

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previous

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Environment overview

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next

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Interaction of BALTRAD and wradlib via ODIM_H5

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+ © 2022. + + By The Open Radar Community. + + Last updated on 21 February 2024. +

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+ + \ No newline at end of file diff --git a/_preview/104/wradlib/README.html b/_preview/104/wradlib/README.html new file mode 100644 index 00000000..c283381e --- /dev/null +++ b/_preview/104/wradlib/README.html @@ -0,0 +1,633 @@ + + + + + + + + Wradlib Tutorial — Project Pythia Cookbook Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
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Wradlib Tutorial

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This is a short overview of wradlib capabilities in 45 minutes. Brace yourself.

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Radar data IO, Visualisation, Gridding and GIS export

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Reading routines for several radar data formats based on xarray are shown. +Data will be read into xarray datasets with easy access for quick analysis and +visualisation. Datasets can be written to ODIM_H5 and CfRadial2-like files. +Easy multiprocessing using DASK can be facilitated. Data is gridded and +exported to Geoinformation Systems (GIS) formats.

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Data Quality Processing and Beamblockage

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A very short overview on the available data quality algorithms with clutter +detection and beamblockage calculations.

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Attenuation Correction using ZPHI-Method

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Already quite aged, the attenuation correction based on the ZPHI-method +(see Testud et. al.) +is still one of the most used algorithms in weather radar. A quick walk through +the necessary steps to derive specific attenuation is shown. The benefits of +specific attenuation derived KDP is shown.

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Quasi Vertical Profiles (QVP)

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A short introduction into QVPs is given.

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Short Overview over wradlib features

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Here we just mention the capabilities of wradlib and emphasize a few of the new +enhancements in recent wradlib versions.

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previous

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Exercice Sample Solution

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next

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An Overview of wradlib

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